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1502.05698 | 41 | A ï¬exible framework This set of tasks is not a deï¬nitive set. The purpose of a simulation-based approach is to provide ï¬exibility and control of the tasksâ construction. We grounded the tasks into language because it is then easier to understand the usefulness of the tasks and to interpret their results. However, our primary goal is to ï¬nd models able to learn to detect and combine patterns in symbolic sequences. One might even want to decrease the intrinsic difï¬culty by removing any lexical variability and ambiguity and reason only over bare symbols, stripped down from their lin- guistic meaning. One could also decorrelate the long-term memory from the reasoning capabilities of systems by, for instance, arranging the supporting facts closer to the questions. In the opposing view, one could instead want to transform the tasks into more realistic stories using annotators or more complex grammars. The set of 20 tasks presented here is a subset of what can be achieved with a simulation. We chose them because they offer a variety of skills that we would like a text reasoning model to have, but we hope researchers from the community will develop more tasks of varying complexity in order to develop and analyze models that try to solve them. Transfer learning across tasks is also a very important goal, beyond the scope of this paper. We have thus made the simulator and code for the tasks publicly available for those purposes. | 1502.05698#41 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 41,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "A ï¬exible framework This set of tasks is not a deï¬nitive set. The purpose of a simulation-based approach is to provide ï¬exibility and control of the tasksâ construction. We grounded the tasks into language because it is then easier to understand the usefulness of the tasks and to interpret their results. However, our primary goal is to ï¬nd models able to learn to detect and combine patterns in symbolic sequences. One might even want to decrease the intrinsic difï¬culty by removing any lexical variability and ambiguity and reason only over bare symbols, stripped down from their lin- guistic meaning. One could also decorrelate the long-term memory from the reasoning capabilities of systems by, for instance, arranging the supporting facts closer to the questions. In the opposing view, one could instead want to transform the tasks into more realistic stories using annotators or more complex grammars. The set of 20 tasks presented here is a subset of what can be achieved with a simulation. We chose them because they offer a variety of skills that we would like a text reasoning model to have, but we hope researchers from the community will develop more tasks of varying complexity in order to develop and analyze models that try to solve them. Transfer learning across tasks is also a very important goal, beyond the scope of this paper. We have thus made the simulator and code for the tasks publicly available for those purposes.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
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] |
1502.05477 | 42 | We start out with a lemma from Kakade & Langford (2002) that shows that the difference in policy performance η(ËÏ)âη(Ï) can be decomposed as a sum of per-timestep advantages. Lemma 1. Given two policies Ï, ËÏ,
nt) = (mt) +E, we t=0 (19)
This expectation is taken over trajectories Ï := (s0, a0, s1, a0, . . . ), and the notation EÏ â¼ËÏ [. . . ] indicates that actions are sampled from ËÏ to generate Ï .
Proof. First note that A,(s,a) = Ey ~p(sâ|s,a) [7(s) + yVn(sâ) â V;-(s)]. Therefore, oo
oo Bae [So 7Anlone ) (20) =0
oo = Ene en r(s:) + Wx (a) vals (21) t=0
=Eqe |- V(80) + Sr (22) t=0
= âE,, [Vr (so)] + Exe > âra (23) t=0
= âη(Ï) + η(ËÏ) (24)
Rearranging, the result follows. | 1502.05477#42 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 42,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "We start out with a lemma from Kakade & Langford (2002) that shows that the difference in policy performance η(ËÏ)âη(Ï) can be decomposed as a sum of per-timestep advantages. Lemma 1. Given two policies Ï, ËÏ,\nnt) = (mt) +E, we t=0 (19)\nThis expectation is taken over trajectories Ï := (s0, a0, s1, a0, . . . ), and the notation EÏ â¼ËÏ [. . . ] indicates that actions are sampled from ËÏ to generate Ï .\nProof. First note that A,(s,a) = Ey ~p(sâ|s,a) [7(s) + yVn(sâ) â V;-(s)]. Therefore, oo\noo Bae [So 7Anlone ) (20) =0\noo = Ene en r(s:) + Wx (a) vals (21) t=0\n=Eqe |- V(80) + Sr (22) t=0\n= âE,, [Vr (so)] + Exe > âra (23) t=0\n= âη(Ï) + η(ËÏ) (24)\nRearranging, the result follows.",
"title": "Trust Region Policy Optimization",
"year": 2015
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] |
1502.05698 | 42 | Testing learning methods Our tasks are designed as a test-bed for learning methods: we provide training and test sets because we intend to evaluate the capability of models to discover how to reason from patterns hidden within them. It could be tempting to hand-code solutions for them or to use existing large-scale QA systems like Cyc (Curtis et al., 2005). They might succeed at solving them, even if our structured SVM results (a cascaded NLP system with hand-built features) show that this is not straightforward; however this is not the tasksâ purpose since those approaches would not be learning to solve them. Our experiments show that some existing machine learning methods are successful on some of the tasks, in particular Memory Networks, for which we introduced some useful extensions (in Sec. A). However, those models still fail on several of the tasks, and use a far stronger form of supervision (using supporting facts) than is typically realistic. | 1502.05698#42 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 42,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Testing learning methods Our tasks are designed as a test-bed for learning methods: we provide training and test sets because we intend to evaluate the capability of models to discover how to reason from patterns hidden within them. It could be tempting to hand-code solutions for them or to use existing large-scale QA systems like Cyc (Curtis et al., 2005). They might succeed at solving them, even if our structured SVM results (a cascaded NLP system with hand-built features) show that this is not straightforward; however this is not the tasksâ purpose since those approaches would not be learning to solve them. Our experiments show that some existing machine learning methods are successful on some of the tasks, in particular Memory Networks, for which we introduced some useful extensions (in Sec. A). However, those models still fail on several of the tasks, and use a far stronger form of supervision (using supporting facts) than is typically realistic.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 43 | = âη(Ï) + η(ËÏ) (24)
Rearranging, the result follows.
Deï¬ne ¯A(s) to be the expected advantage of ËÏ over Ï at state s:
¯A(s) = Eaâ¼ËÏ(·|s) [AÏ(s, a)] . (25)
Now Lemma 1 can be written as follows:
H(t) = 9(7) + Eve tA ] (26) t=0
Note that LÏ can be written as
Ly (7) = n(m) + Ewe > â40 (27) t=0
The difference in these equations is whether the states are sampled using 7 or 7. To bound the difference between 7)(7) and L,,(), we will bound the difference arising from each timestep. To do this, we first need to introduce a measure of how much z and 7 agree. Specifically, weâll couple the policies, so that they define a joint distribution over pairs of actions. Definition 1. (7, 7) is an a-coupled policy pair if it defines a joint distribution (a, @)|s, such that P(a # a|s) < a for all s. 7 and 7 will denote the marginal distributions of a and 4G, respectively.
Trust Region Policy Optimization | 1502.05477#43 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 43,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "= âη(Ï) + η(ËÏ) (24)\nRearranging, the result follows.\nDeï¬ne ¯A(s) to be the expected advantage of ËÏ over Ï at state s:\n¯A(s) = Eaâ¼ËÏ(·|s) [AÏ(s, a)] . (25)\nNow Lemma 1 can be written as follows:\nH(t) = 9(7) + Eve tA ] (26) t=0\nNote that LÏ can be written as\nLy (7) = n(m) + Ewe > â40 (27) t=0\nThe difference in these equations is whether the states are sampled using 7 or 7. To bound the difference between 7)(7) and L,,(), we will bound the difference arising from each timestep. To do this, we first need to introduce a measure of how much z and 7 agree. Specifically, weâll couple the policies, so that they define a joint distribution over pairs of actions. Definition 1. (7, 7) is an a-coupled policy pair if it defines a joint distribution (a, @)|s, such that P(a # a|s) < a for all s. 7 and 7 will denote the marginal distributions of a and 4G, respectively.\nTrust Region Policy Optimization",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 43 | These datasets are not yet solved. Future research should aim to minimize the amount of required supervision, as well as the number of training examples needed to solve a new task, to move closer to the task transfer capabilities of humans. That is, in the weakly supervised case with only 1000 training examples or less there is no known general (i.e. non-hand engineered) method that solves the tasks. Further, importantly, our hope is that a feedback loop of developing more challenging tasks, and then algorithms that can solve them, leads us to fruitful research directions.
Note that these tasks are not a substitute for real data, but should complement them, especially when developing and analysing algorithms. There are many complementary real-world datasets, see for example Hermann et al. (2015); Bordes et al. (2015); Hill et al. (2015). That is, even if a method works well on our 20 tasks, it should be shown to be useful on real data as well.
9
# Under review as a conference paper at ICLR 2016 | 1502.05698#43 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 43,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "These datasets are not yet solved. Future research should aim to minimize the amount of required supervision, as well as the number of training examples needed to solve a new task, to move closer to the task transfer capabilities of humans. That is, in the weakly supervised case with only 1000 training examples or less there is no known general (i.e. non-hand engineered) method that solves the tasks. Further, importantly, our hope is that a feedback loop of developing more challenging tasks, and then algorithms that can solve them, leads us to fruitful research directions.\nNote that these tasks are not a substitute for real data, but should complement them, especially when developing and analysing algorithms. There are many complementary real-world datasets, see for example Hermann et al. (2015); Bordes et al. (2015); Hill et al. (2015). That is, even if a method works well on our 20 tasks, it should be shown to be useful on real data as well.\n9\n# Under review as a conference paper at ICLR 2016",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 44 | Trust Region Policy Optimization
Computationally, α-coupling means that if we randomly choose a seed for our random number generator, and then we sample from each of Ï and ËÏ after setting that seed, the results will agree for at least fraction 1 â α of seeds. Lemma 2. Given that Ï, ËÏ are α-coupled policies, for all s,
|A(s)|
|A(s)| < 2a max |A,(s,a)| (28)
Proof.
A(s) = Eawe [An(8,@)] = Eva,aycn,a) [An(8,@) â Ax(s,a)] since Equa [Ax(s,a)] =0 (29) = P(aF G|s)E(a,a)~(n,%)|aza [An (8,4) â An(s, @)] (30) |A(s)| < a@-2max|A,(s, a)| 1)
Lemma 3. Let (Ï, ËÏ) be an α-coupled policy pair. Then
[Ese [A(sz)] â Eg,vn [A(sz)] | < 2a max A(s) < da(1 â (1 -a)') max|A,(s, a)| (32) | 1502.05477#44 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 44,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "Trust Region Policy Optimization\nComputationally, α-coupling means that if we randomly choose a seed for our random number generator, and then we sample from each of Ï and ËÏ after setting that seed, the results will agree for at least fraction 1 â α of seeds. Lemma 2. Given that Ï, ËÏ are α-coupled policies, for all s,\n|A(s)|\n|A(s)| < 2a max |A,(s,a)| (28)\nProof.\nA(s) = Eawe [An(8,@)] = Eva,aycn,a) [An(8,@) â Ax(s,a)] since Equa [Ax(s,a)] =0 (29) = P(aF G|s)E(a,a)~(n,%)|aza [An (8,4) â An(s, @)] (30) |A(s)| < a@-2max|A,(s, a)| 1)\nLemma 3. Let (Ï, ËÏ) be an α-coupled policy pair. Then\n[Ese [A(sz)] â Eg,vn [A(sz)] | < 2a max A(s) < da(1 â (1 -a)') max|A,(s, a)| (32)",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 44 | 9
# Under review as a conference paper at ICLR 2016
Impact Since being online, the bAbI tasks have already directly inï¬uenced the development of several promising new algorithms, including weakly supervised end-to-end Memory Networks (MemN2N) of Sukhbaatar et al. (2015), Dynamic Memory Networks of Kumar et al. (2015), and the Neural Reasoner (Peng et al., 2015). MemN2N has since been shown to perform well on some real-world tasks (Hill et al., 2015).
# REFERENCES
Bache, K. and Lichman, M. http://archive.ics.uci.edu/ml. UCI machine learning repository, 2013. URL
Berant, Jonathan, Chou, Andrew, Frostig, Roy, and Liang, Percy. Semantic parsing on freebase from question-answer pairs. In EMNLP, pp. 1533â1544, 2013.
Berant, Jonathan, Srikumar, Vivek, Chen, Pei-Chun, Huang, Brad, Manning, Christopher D, Van- der Linden, Abby, Harding, Brittany, and Clark, Peter. Modeling biological processes for reading comprehension. In Proc. EMNLP, 2014. | 1502.05698#44 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 44,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "9\n# Under review as a conference paper at ICLR 2016\nImpact Since being online, the bAbI tasks have already directly inï¬uenced the development of several promising new algorithms, including weakly supervised end-to-end Memory Networks (MemN2N) of Sukhbaatar et al. (2015), Dynamic Memory Networks of Kumar et al. (2015), and the Neural Reasoner (Peng et al., 2015). MemN2N has since been shown to perform well on some real-world tasks (Hill et al., 2015).\n# REFERENCES\nBache, K. and Lichman, M. http://archive.ics.uci.edu/ml. UCI machine learning repository, 2013. URL\nBerant, Jonathan, Chou, Andrew, Frostig, Roy, and Liang, Percy. Semantic parsing on freebase from question-answer pairs. In EMNLP, pp. 1533â1544, 2013.\nBerant, Jonathan, Srikumar, Vivek, Chen, Pei-Chun, Huang, Brad, Manning, Christopher D, Van- der Linden, Abby, Harding, Brittany, and Clark, Peter. Modeling biological processes for reading comprehension. In Proc. EMNLP, 2014.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
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1502.05477 | 45 | Proof. Given the coupled policy pair (Ï, ËÏ), we can also obtain a coupling over the trajectory distributions produced by Ï and ËÏ, respectively. Namely, we have pairs of trajectories Ï, ËÏ , where Ï is obtained by taking actions from Ï, and ËÏ is obtained by taking actions from ËÏ, where the same random seed is used to generate both trajectories. We will consider the advantage of ËÏ over Ï at timestep t, and decompose this expectation based on whether Ï agrees with ËÏ at all timesteps i < t.
Let n; denote the number of times that a; 4 @; for i < t, i.e., the number of times that 7 and 7 disagree before timestep t.
Es,x% [A(s:)] = P(ri = 0)Es,~#
,-0 [A(s:)] + P(r > O)Es, vz jnes0 [A(se)] (33)
The expectation decomposes similarly for actions are sampled using Ï:
Es,or [A(si)] = P(re = O)Es, .n|n,=0 [A(se)] + P(re > OES, va|n,>0 [A(se)] (34)
Note that the nt = 0 terms are equal: | 1502.05477#45 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 45,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "Proof. Given the coupled policy pair (Ï, ËÏ), we can also obtain a coupling over the trajectory distributions produced by Ï and ËÏ, respectively. Namely, we have pairs of trajectories Ï, ËÏ , where Ï is obtained by taking actions from Ï, and ËÏ is obtained by taking actions from ËÏ, where the same random seed is used to generate both trajectories. We will consider the advantage of ËÏ over Ï at timestep t, and decompose this expectation based on whether Ï agrees with ËÏ at all timesteps i < t.\nLet n; denote the number of times that a; 4 @; for i < t, i.e., the number of times that 7 and 7 disagree before timestep t.\nEs,x% [A(s:)] = P(ri = 0)Es,~#\n,-0 [A(s:)] + P(r > O)Es, vz jnes0 [A(se)] (33)\nThe expectation decomposes similarly for actions are sampled using Ï:\nEs,or [A(si)] = P(re = O)Es, .n|n,=0 [A(se)] + P(re > OES, va|n,>0 [A(se)] (34)\nNote that the nt = 0 terms are equal:",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 45 | Bordes, Antoine, Usunier, Nicolas, Collobert, Ronan, and Weston, Jason. Towards understanding situated natural language. In AISTATS, 2010.
Bordes, Antoine, Usunier, Nicolas, Chopra, Sumit, and Weston, Jason. Large-scale simple question answering with memory networks. arXiv preprint arXiv:1506.02075, 2015.
Chen, David L and Mooney, Raymond J. Learning to interpret natural language navigation instruc- tions from observations. San Francisco, CA, pp. 859â865, 2011.
Collobert, Ronan, Weston, Jason, Bottou, L´eon, Karlen, Michael, Kavukcuoglu, Koray, and Kuksa, Pavel. Natural language processing (almost) from scratch. The Journal of Machine Learning Research, 12:2493â2537, 2011.
Curtis, Jon, Matthews, Gavin, and Baxter, David. On the effective use of cyc in a question answering system. In IJCAI Workshop on Knowledge and Reasoning for Answering Questions, pp. 61â70, 2005.
Fader, Anthony, Zettlemoyer, Luke, and Etzioni, Oren. Paraphrase-driven learning for open question answering. In ACL, pp. 1608â1618, 2013. | 1502.05698#45 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 45,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Bordes, Antoine, Usunier, Nicolas, Collobert, Ronan, and Weston, Jason. Towards understanding situated natural language. In AISTATS, 2010.\nBordes, Antoine, Usunier, Nicolas, Chopra, Sumit, and Weston, Jason. Large-scale simple question answering with memory networks. arXiv preprint arXiv:1506.02075, 2015.\nChen, David L and Mooney, Raymond J. Learning to interpret natural language navigation instruc- tions from observations. San Francisco, CA, pp. 859â865, 2011.\nCollobert, Ronan, Weston, Jason, Bottou, L´eon, Karlen, Michael, Kavukcuoglu, Koray, and Kuksa, Pavel. Natural language processing (almost) from scratch. The Journal of Machine Learning Research, 12:2493â2537, 2011.\nCurtis, Jon, Matthews, Gavin, and Baxter, David. On the effective use of cyc in a question answering system. In IJCAI Workshop on Knowledge and Reasoning for Answering Questions, pp. 61â70, 2005.\nFader, Anthony, Zettlemoyer, Luke, and Etzioni, Oren. Paraphrase-driven learning for open question answering. In ACL, pp. 1608â1618, 2013.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 46 | Note that the nt = 0 terms are equal:
Ey, vi|me=0 [A(s:)] = Esewm|ne=0 [A(s:)] , (35)
because n; = 0 indicates that 7 and 7 agreed on all timesteps less than t. Subtracting Equations and (34), we get Ex,x% [A(s:)] âEs,~x [A(s:)] = P(ne > 0) (Es,n#|n.50 [A(se)] â Es,.rjnes0 [A(se)])
# Estâ¼ËÏ
By deï¬nition of α, P (Ï, ËÏ agree at timestep i) ⥠1 â α, so P (nt = 0) ⥠(1 â α)t, and
P (nt > 0) ⤠1 â (1 â α)t (37)
Next, note that
Ex, ~#
.>0 [A(Se)] â Es,~x|n,>0 [A(s2)] | E,,.#|n,>0 [A(sz)] | + |Es,am|n.>0 [A(s:)] | (38) < < da max |A,(s, a)| (39)
Where the second inequality follows from Lemma 3. | 1502.05477#46 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 46,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "Note that the nt = 0 terms are equal:\nEy, vi|me=0 [A(s:)] = Esewm|ne=0 [A(s:)] , (35)\nbecause n; = 0 indicates that 7 and 7 agreed on all timesteps less than t. Subtracting Equations and (34), we get Ex,x% [A(s:)] âEs,~x [A(s:)] = P(ne > 0) (Es,n#|n.50 [A(se)] â Es,.rjnes0 [A(se)])\n# Estâ¼ËÏ\nBy deï¬nition of α, P (Ï, ËÏ agree at timestep i) ⥠1 â α, so P (nt = 0) ⥠(1 â α)t, and\nP (nt > 0) ⤠1 â (1 â α)t (37)\nNext, note that\nEx, ~#\n.>0 [A(Se)] â Es,~x|n,>0 [A(s2)] | E,,.#|n,>0 [A(sz)] | + |Es,am|n.>0 [A(s:)] | (38) < < da max |A,(s, a)| (39)\nWhere the second inequality follows from Lemma 3.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 46 | Fader, Anthony, Zettlemoyer, Luke, and Etzioni, Oren. Open question answering over curated and extracted knowledge bases. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1156â1165. ACM, 2014.
Graves, Alex, Wayne, Greg, and Danihelka, Ivo. Neural turing machines. arXiv preprint arXiv:1410.5401, 2014.
Halevy, Alon, Norvig, Peter, and Pereira, Fernando. The unreasonable effectiveness of data. Intelli- gent Systems, IEEE, 24(2):8â12, 2009.
Hermann, Karl Moritz, KoËcisk´y, Tom´aËs, Grefenstette, Edward, Espeholt, Lasse, Kay, Will, Teaching machines to read and compre- URL Suleyman, Mustafa, and Blunsom, Phil. hend. http://arxiv.org/abs/1506.03340. In Advances in Neural Information Processing Systems (NIPS), 2015. | 1502.05698#46 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 46,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Fader, Anthony, Zettlemoyer, Luke, and Etzioni, Oren. Open question answering over curated and extracted knowledge bases. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1156â1165. ACM, 2014.\nGraves, Alex, Wayne, Greg, and Danihelka, Ivo. Neural turing machines. arXiv preprint arXiv:1410.5401, 2014.\nHalevy, Alon, Norvig, Peter, and Pereira, Fernando. The unreasonable effectiveness of data. Intelli- gent Systems, IEEE, 24(2):8â12, 2009.\nHermann, Karl Moritz, KoËcisk´y, Tom´aËs, Grefenstette, Edward, Espeholt, Lasse, Kay, Will, Teaching machines to read and compre- URL Suleyman, Mustafa, and Blunsom, Phil. hend. http://arxiv.org/abs/1506.03340. In Advances in Neural Information Processing Systems (NIPS), 2015.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
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] |
1502.05477 | 47 | Where the second inequality follows from Lemma 3.
and Equation (39) into Equation (36), we get [Bsa [A(se)] â Es.xe [A(se)]| < a(t ~ (1 ~ a)")max|An(s,0)| (40)
Plugging Equation (37) and Equation (39) into Equation (36), we get
(36)
(40)
Trust Region Policy Optimization
The preceding Lemma bounds the difference in expected advantage at each timestep t. We can sum over time to bound the difference between (7) and L,.(7). Subtracting Equation and Equation 27, and defining « = max, ,_ |A;(s,a)|,
oo Init) â Ln #)| = 77! |Erwz [A(se)] â Erm [A(se)] | (41) t=0
t=0
< 5-4! - 4ea(1 â (1 â a)') (42) t=0
1 1
da?ye T=) 10a) da?ye <a
= (44)
⤠(45)
Last, to replace α by the total variation divergence, we need to use the correspondence between TV divergence and coupled random variables: | 1502.05477#47 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 47,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "Where the second inequality follows from Lemma 3.\nand Equation (39) into Equation (36), we get [Bsa [A(se)] â Es.xe [A(se)]| < a(t ~ (1 ~ a)\")max|An(s,0)| (40)\nPlugging Equation (37) and Equation (39) into Equation (36), we get\n(36)\n(40)\nTrust Region Policy Optimization\nThe preceding Lemma bounds the difference in expected advantage at each timestep t. We can sum over time to bound the difference between (7) and L,.(7). Subtracting Equation and Equation 27, and defining « = max, ,_ |A;(s,a)|,\noo Init) â Ln #)| = 77! |Erwz [A(se)] â Erm [A(se)] | (41) t=0\nt=0\n< 5-4! - 4ea(1 â (1 â a)') (42) t=0\n1 1\nda?ye T=) 10a) da?ye <a\n= (44)\n⤠(45)\nLast, to replace α by the total variation divergence, we need to use the correspondence between TV divergence and coupled random variables:",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 47 | Hill, Felix, Bordes, Antoine, Chopra, Sumit, and Weston, Jason. The goldilocks principle: Reading childrenâs books with explicit memory representation s. arXiv preprint arXiv:1511.02301, 2015.
Hochreiter, Sepp and Schmidhuber, J¨urgen. Long short-term memory. Neural computation, 9(8): 1735â1780, 1997.
Kumar, Ankit, Irsoy, Ozan, Su, Jonathan, Bradbury, James, English, Robert, Pierce, ËBrian, On- druska, Peter, Gulrajani, Ishaan, and Socher, Richard. Ask me anything: Dynamic memory net- works for natural language processing. http://arxiv.org/abs/1506.07285, 2015.
10
# Under review as a conference paper at ICLR 2016
Levesque, Hector J, Davis, Ernest, and Morgenstern, Leora. The winograd schema challenge. In AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning, 2011.
Liang, Percy. Lambda dependency-based compositional semantics. arXiv preprint arXiv:1309.4408, 2013. | 1502.05698#47 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 47,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Hill, Felix, Bordes, Antoine, Chopra, Sumit, and Weston, Jason. The goldilocks principle: Reading childrenâs books with explicit memory representation s. arXiv preprint arXiv:1511.02301, 2015.\nHochreiter, Sepp and Schmidhuber, J¨urgen. Long short-term memory. Neural computation, 9(8): 1735â1780, 1997.\nKumar, Ankit, Irsoy, Ozan, Su, Jonathan, Bradbury, James, English, Robert, Pierce, ËBrian, On- druska, Peter, Gulrajani, Ishaan, and Socher, Richard. Ask me anything: Dynamic memory net- works for natural language processing. http://arxiv.org/abs/1506.07285, 2015.\n10\n# Under review as a conference paper at ICLR 2016\nLevesque, Hector J, Davis, Ernest, and Morgenstern, Leora. The winograd schema challenge. In AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning, 2011.\nLiang, Percy. Lambda dependency-based compositional semantics. arXiv preprint arXiv:1309.4408, 2013.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 48 | = (44)
⤠(45)
Last, to replace α by the total variation divergence, we need to use the correspondence between TV divergence and coupled random variables:
Suppose px and py are distributions with Dry(px || py) = a. Then there exists a joint distribution (X, Y) whose marginals are px, py, for which X = Y with probability 1 â a.
See (Levin et al., 2009), Proposition 4.7.
It follows that if we have two policies 7 and 7 such that max, Dry (7(-|s) || 7(-|s)) < @, then we can define an a-coupled policy pair (7, 7) with appropriate marginals. Taking a = max, Dry (7(-|s) || #(-|s)) < @ in Equation (45), Theorem|]] follows.
# B Perturbation Theory Proof of Policy Improvement Bound
We also provide an alternative proof of Theorem 1 using perturbation theory. | 1502.05477#48 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 48,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "= (44)\n⤠(45)\nLast, to replace α by the total variation divergence, we need to use the correspondence between TV divergence and coupled random variables:\nSuppose px and py are distributions with Dry(px || py) = a. Then there exists a joint distribution (X, Y) whose marginals are px, py, for which X = Y with probability 1 â a.\nSee (Levin et al., 2009), Proposition 4.7.\nIt follows that if we have two policies 7 and 7 such that max, Dry (7(-|s) || 7(-|s)) < @, then we can define an a-coupled policy pair (7, 7) with appropriate marginals. Taking a = max, Dry (7(-|s) || #(-|s)) < @ in Equation (45), Theorem|]] follows.\n# B Perturbation Theory Proof of Policy Improvement Bound\nWe also provide an alternative proof of Theorem 1 using perturbation theory.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 48 | Liang, Percy. Lambda dependency-based compositional semantics. arXiv preprint arXiv:1309.4408, 2013.
Liang, Percy, Jordan, Michael I, and Klein, Dan. Learning dependency-based compositional seman- tics. Computational Linguistics, 39(2):389â446, 2013.
Minsky, Marvin and Papert, Seymour. Perceptron: an introduction to computational geometry. The MIT Press, Cambridge, expanded edition, 19:88, 1969.
Montfort, Nick. Twisty Little Passages: an approach to interactive ï¬ction. Mit Press, 2005.
M¨uller, K-R, Smola, Alex J, R¨atsch, Gunnar, Sch¨olkopf, Bernhard, Kohlmorgen, Jens, and Vapnik, Vladimir. Predicting time series with support vector machines. In Artiï¬cial Neural NetworksI- CANNâ97, pp. 999â1004. Springer, 1997.
Ng, Andrew Y, Jordan, Michael I, Weiss, Yair, et al. On spectral clustering: Analysis and an algo- rithm. Advances in neural information processing systems, 2:849â856, 2002. | 1502.05698#48 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 48,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Liang, Percy. Lambda dependency-based compositional semantics. arXiv preprint arXiv:1309.4408, 2013.\nLiang, Percy, Jordan, Michael I, and Klein, Dan. Learning dependency-based compositional seman- tics. Computational Linguistics, 39(2):389â446, 2013.\nMinsky, Marvin and Papert, Seymour. Perceptron: an introduction to computational geometry. The MIT Press, Cambridge, expanded edition, 19:88, 1969.\nMontfort, Nick. Twisty Little Passages: an approach to interactive ï¬ction. Mit Press, 2005.\nM¨uller, K-R, Smola, Alex J, R¨atsch, Gunnar, Sch¨olkopf, Bernhard, Kohlmorgen, Jens, and Vapnik, Vladimir. Predicting time series with support vector machines. In Artiï¬cial Neural NetworksI- CANNâ97, pp. 999â1004. Springer, 1997.\nNg, Andrew Y, Jordan, Michael I, Weiss, Yair, et al. On spectral clustering: Analysis and an algo- rithm. Advances in neural information processing systems, 2:849â856, 2002.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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1502.05477 | 49 | # B Perturbation Theory Proof of Policy Improvement Bound
We also provide an alternative proof of Theorem 1 using perturbation theory.
Proof. Let G = (1+γPÏ +(γPÏ)2+. . . ) = (1âγPÏ)â1, and similarly Let ËG = (1+γPËÏ +(γPËÏ)2+. . . ) = (1âγPËÏ)â1. We will use the convention that Ï (a density on state space) is a vector and r (a reward function on state space) is a dual vector (i.e., linear functional on vectors), thus rÏ is a scalar meaning the expected reward under density Ï. Note that η(Ï) = rGÏ0, and η(ËÏ) = c ËGÏ0. Let â = PËÏ â PÏ. We want to bound η(ËÏ) â η(Ï) = r( ËG â G)Ï0. We start with some standard perturbation theory manipulations.
Gâ1 â ËGâ1 = (1 â γPÏ) â (1 â γPËÏ) = γâ. (46)
Left multiply by G and right multiply by ËG. | 1502.05477#49 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 49,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "# B Perturbation Theory Proof of Policy Improvement Bound\nWe also provide an alternative proof of Theorem 1 using perturbation theory.\nProof. Let G = (1+γPÏ +(γPÏ)2+. . . ) = (1âγPÏ)â1, and similarly Let ËG = (1+γPËÏ +(γPËÏ)2+. . . ) = (1âγPËÏ)â1. We will use the convention that Ï (a density on state space) is a vector and r (a reward function on state space) is a dual vector (i.e., linear functional on vectors), thus rÏ is a scalar meaning the expected reward under density Ï. Note that η(Ï) = rGÏ0, and η(ËÏ) = c ËGÏ0. Let â = PËÏ â PÏ. We want to bound η(ËÏ) â η(Ï) = r( ËG â G)Ï0. We start with some standard perturbation theory manipulations.\nGâ1 â ËGâ1 = (1 â γPÏ) â (1 â γPËÏ) = γâ. (46)\nLeft multiply by G and right multiply by ËG.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 49 | Peng, Baolin, Lu, Zhengdong, Li, Hang, and Wong, Kam-Fai. Towards neural network-based rea- soning. arXiv preprint arXiv:1508.05508, 2015.
Raghunathan, Karthik, Lee, Heeyoung, Rangarajan, Sudarshan, Chambers, Nathanael, Surdeanu, Mihai, Jurafsky, Dan, and Manning, Christopher. A multi-pass sieve for coreference resolution. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 492â501. Association for Computational Linguistics, 2010.
Richardson, Matthew, Burges, Christopher JC, and Renshaw, Erin. Mctest: A challenge dataset for the open-domain machine comprehension of text. In EMNLP, pp. 193â203, 2013.
Rumelhart, David E, Hinton, Geoffrey E, and Williams, Ronald J. Learning internal representations by error propagation. Technical report, DTIC Document, 1985.
Soon, Wee Meng, Ng, Hwee Tou, and Lim, Daniel Chung Yong. A machine learning approach to coreference resolution of noun phrases. Computational linguistics, 27(4):521â544, 2001. | 1502.05698#49 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 49,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Peng, Baolin, Lu, Zhengdong, Li, Hang, and Wong, Kam-Fai. Towards neural network-based rea- soning. arXiv preprint arXiv:1508.05508, 2015.\nRaghunathan, Karthik, Lee, Heeyoung, Rangarajan, Sudarshan, Chambers, Nathanael, Surdeanu, Mihai, Jurafsky, Dan, and Manning, Christopher. A multi-pass sieve for coreference resolution. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 492â501. Association for Computational Linguistics, 2010.\nRichardson, Matthew, Burges, Christopher JC, and Renshaw, Erin. Mctest: A challenge dataset for the open-domain machine comprehension of text. In EMNLP, pp. 193â203, 2013.\nRumelhart, David E, Hinton, Geoffrey E, and Williams, Ronald J. Learning internal representations by error propagation. Technical report, DTIC Document, 1985.\nSoon, Wee Meng, Ng, Hwee Tou, and Lim, Daniel Chung Yong. A machine learning approach to coreference resolution of noun phrases. Computational linguistics, 27(4):521â544, 2001.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 50 | Left multiply by G and right multiply by ËG.
ËG â G = γGâ ËG ËG = G + γGâ ËG (47)
Substituting the right-hand side into ËG gives
ËG = G + γGâG + γ2GâGâ ËG (48)
So we have
η(ËÏ) â η(Ï) = r( ËG â G)Ï = γrGâGÏ0 + γ2rGâGâ ËGÏ0 (49)
Let us ï¬rst consider the leading term γrGâGÏ0. Note that rG = v, i.e., the inï¬nite-horizon state-value function. Also note that GÏ0 = ÏÏ. Thus we can write γcGâGÏ0 = γvâÏÏ. We will show that this expression equals the expected
Trust Region Policy Optimization
advantage LÏ(ËÏ) â LÏ(Ï). | 1502.05477#50 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 50,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "Left multiply by G and right multiply by ËG.\nËG â G = γGâ ËG ËG = G + γGâ ËG (47)\nSubstituting the right-hand side into ËG gives\nËG = G + γGâG + γ2GâGâ ËG (48)\nSo we have\nη(ËÏ) â η(Ï) = r( ËG â G)Ï = γrGâGÏ0 + γ2rGâGâ ËGÏ0 (49)\nLet us ï¬rst consider the leading term γrGâGÏ0. Note that rG = v, i.e., the inï¬nite-horizon state-value function. Also note that GÏ0 = ÏÏ. Thus we can write γcGâGÏ0 = γvâÏÏ. We will show that this expression equals the expected\nTrust Region Policy Optimization\nadvantage LÏ(ËÏ) â LÏ(Ï).",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 50 | Sukhbaatar, Sainbayar, Szlam, Arthur, Weston, Jason, and Fergus, Rob. End-to-end memory net- works. Proceedings of NIPS, 2015.
Sutskever, Ilya, Vinyals, Oriol, and Le, Quoc VV. Sequence to sequence learning with neural net- works. In Advances in Neural Information Processing Systems, pp. 3104â3112, 2014.
UzZaman, Naushad, Llorens, Hector, Allen, James, Derczynski, Leon, Verhagen, Marc, and Puste- jovsky, James. Tempeval-3: Evaluating events, time expressions, and temporal relations. arXiv preprint arXiv:1206.5333, 2012.
Weston, Jason, Chopra, Sumit, and Bordes, Antoine. Memory networks. CoRR, abs/1410.3916, 2014.
Winograd, Terry. Understanding natural language. Cognitive psychology, 3(1):1â191, 1972.
Yao, Xuchen, Berant, Jonathan, and Van Durme, Benjamin. Freebase qa: Information extraction or semantic parsing? ACL 2014, pp. 82, 2014. | 1502.05698#50 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 50,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Sukhbaatar, Sainbayar, Szlam, Arthur, Weston, Jason, and Fergus, Rob. End-to-end memory net- works. Proceedings of NIPS, 2015.\nSutskever, Ilya, Vinyals, Oriol, and Le, Quoc VV. Sequence to sequence learning with neural net- works. In Advances in Neural Information Processing Systems, pp. 3104â3112, 2014.\nUzZaman, Naushad, Llorens, Hector, Allen, James, Derczynski, Leon, Verhagen, Marc, and Puste- jovsky, James. Tempeval-3: Evaluating events, time expressions, and temporal relations. arXiv preprint arXiv:1206.5333, 2012.\nWeston, Jason, Chopra, Sumit, and Bordes, Antoine. Memory networks. CoRR, abs/1410.3916, 2014.\nWinograd, Terry. Understanding natural language. Cognitive psychology, 3(1):1â191, 1972.\nYao, Xuchen, Berant, Jonathan, and Van Durme, Benjamin. Freebase qa: Information extraction or semantic parsing? ACL 2014, pp. 82, 2014.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 51 | Trust Region Policy Optimization
advantage LÏ(ËÏ) â LÏ(Ï).
L,,(%) â Ly (7) = Da Pal La (a|s) â r(a|s))Az(s, a) = 5 nls) 3 (rolls) ~ nla) |r) + Yl) a0) ~ ol) = a oD 7 (als) â (als) p(s'|s, a)yu(sâ) = Toul Slr! s'|s) â pz(s'|s))yu(sâ) = ywAp,
(50)
Next let us bound the O(â2) term γ2rGâGâ ËGÏ. First we consider the product γrGâ = γvâ. Consider the component s of this dual vector.
I(ywA)s| = Ss.) â m(s,4))Qx(s,0) =|S-((s,a) â 2(s,a))A,(s,@) < So lF(s,@) â1(s,a)|- max|A,(s,a)| a < 20⬠(51) | 1502.05477#51 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 51,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "Trust Region Policy Optimization\nadvantage LÏ(ËÏ) â LÏ(Ï).\nL,,(%) â Ly (7) = Da Pal La (a|s) â r(a|s))Az(s, a) = 5 nls) 3 (rolls) ~ nla) |r) + Yl) a0) ~ ol) = a oD 7 (als) â (als) p(s'|s, a)yu(sâ) = Toul Slr! s'|s) â pz(s'|s))yu(sâ) = ywAp,\n(50)\nNext let us bound the O(â2) term γ2rGâGâ ËGÏ. First we consider the product γrGâ = γvâ. Consider the component s of this dual vector.\nI(ywA)s| = Ss.) â m(s,4))Qx(s,0) =|S-((s,a) â 2(s,a))A,(s,@) < So lF(s,@) â1(s,a)|- max|A,(s,a)| a < 20⬠(51)",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 51 | Yao, Xuchen, Berant, Jonathan, and Van Durme, Benjamin. Freebase qa: Information extraction or semantic parsing? ACL 2014, pp. 82, 2014.
Yu, Mo, Gormley, Matthew R, and Dredze, Mark. Factor-based compositional embedding models. NIPS 2014 workshop on Learning Semantics, 2014.
Zhu, Xiaojin, Ghahramani, Zoubin, Lafferty, John, et al. Semi-supervised learning using gaussian ï¬elds and harmonic functions. In ICML, volume 3, pp. 912â919, 2003.
11
# Under review as a conference paper at ICLR 2016
# A EXTENSIONS TO MEMORY NETWORKS
Memory Networks Weston et al. (2014) are a promising class of models, shown to perform well at QA, that we can apply to our tasks. They consist of a memory m (an array of objects indexed by mi) and four potentially learnable components I, G, O and R that are executed given an input:
I: (input feature map) â convert input sentence x to an internal feature representation I(x). G: (generalization) â update the current memory state m given the new input: mi =
G(mi, I(x), m), âi. | 1502.05698#51 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 51,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Yao, Xuchen, Berant, Jonathan, and Van Durme, Benjamin. Freebase qa: Information extraction or semantic parsing? ACL 2014, pp. 82, 2014.\nYu, Mo, Gormley, Matthew R, and Dredze, Mark. Factor-based compositional embedding models. NIPS 2014 workshop on Learning Semantics, 2014.\nZhu, Xiaojin, Ghahramani, Zoubin, Lafferty, John, et al. Semi-supervised learning using gaussian ï¬elds and harmonic functions. In ICML, volume 3, pp. 912â919, 2003.\n11\n# Under review as a conference paper at ICLR 2016\n# A EXTENSIONS TO MEMORY NETWORKS\nMemory Networks Weston et al. (2014) are a promising class of models, shown to perform well at QA, that we can apply to our tasks. They consist of a memory m (an array of objects indexed by mi) and four potentially learnable components I, G, O and R that are executed given an input:\nI: (input feature map) â convert input sentence x to an internal feature representation I(x). G: (generalization) â update the current memory state m given the new input: mi =\nG(mi, I(x), m), âi.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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1502.05477 | 52 | where the last line used the definition of the total-variation divergence, and the definition of ⬠= max,,_|A,(s,a)|. We bound the other portion GAG*/p using the ¢; operator norm
|All]; = sup wf = veh \ (52)
where we have that ||G'l|1 = ||G|1 = 1/(1 â 7) and ||A]|1 = 2a. That gives
|GAGpll1 < GU sAllilEllallalls 1 1 = -2a-â-1 (53) 1-yÂ¥ 1-yÂ¥
So we have that
Y|rGAGAGp| < yl17GA oo||GAG pl < 7lvAl]ool|GAG pl < 9 2a <7: 2ae- ~ââ, (ly)? Aye : =4 pe (54)
# C Efï¬ciently Solving the Trust-Region Constrained Optimization Problem
This section describes how to efï¬ciently approximately solve the following constrained optimization problem, which we must solve at each iteration of TRPO:
maximize L(θ) subject to DKL(θold, θ) ⤠δ. (55)
Trust Region Policy Optimization | 1502.05477#52 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 52,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "where the last line used the definition of the total-variation divergence, and the definition of ⬠= max,,_|A,(s,a)|. We bound the other portion GAG*/p using the ¢; operator norm\n|All]; = sup wf = veh \\ (52)\nwhere we have that ||G'l|1 = ||G|1 = 1/(1 â 7) and ||A]|1 = 2a. That gives\n|GAGpll1 < GU sAllilEllallalls 1 1 = -2a-â-1 (53) 1-yÂ¥ 1-yÂ¥\nSo we have that\nY|rGAGAGp| < yl17GA oo||GAG pl < 7lvAl]ool|GAG pl < 9 2a <7: 2ae- ~ââ, (ly)? Aye : =4 pe (54)\n# C Efï¬ciently Solving the Trust-Region Constrained Optimization Problem\nThis section describes how to efï¬ciently approximately solve the following constrained optimization problem, which we must solve at each iteration of TRPO:\nmaximize L(θ) subject to DKL(θold, θ) ⤠δ. (55)\nTrust Region Policy Optimization",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 52 | G(mi, I(x), m), âi.
O: (output feature map) â compute output o given the new input and the memory: o = O(I(x), m).
R: (response) â ï¬nally, decode output features o to give the ï¬nal textual response to the user: r = R(o).
Potentially, component I can make use of standard pre-processing, e.g., parsing and entity resolu- tion, but the simplest form is to do no processing at all. The simplest form of G is store the new incoming example in an empty memory slot, and leave the rest of the memory untouched. Thus, in Weston et al. (2014) the actual implementation used is exactly this simple form, where the bulk of the work is in the O and R components. The former is responsible for reading from memory and performing inference, e.g., calculating what are the relevant memories to answer a question, and the latter for producing the actual wording of the answer given O.
The O module produces output features by ï¬nding k supporting memories given x. They use k = 2. For k = 1 the highest scoring supporting memory is retrieved with:
o1 = O1(x, m) = arg max i=1,...,N sO(x, mi) (1) | 1502.05698#52 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 52,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "G(mi, I(x), m), âi.\nO: (output feature map) â compute output o given the new input and the memory: o = O(I(x), m).\nR: (response) â ï¬nally, decode output features o to give the ï¬nal textual response to the user: r = R(o).\nPotentially, component I can make use of standard pre-processing, e.g., parsing and entity resolu- tion, but the simplest form is to do no processing at all. The simplest form of G is store the new incoming example in an empty memory slot, and leave the rest of the memory untouched. Thus, in Weston et al. (2014) the actual implementation used is exactly this simple form, where the bulk of the work is in the O and R components. The former is responsible for reading from memory and performing inference, e.g., calculating what are the relevant memories to answer a question, and the latter for producing the actual wording of the answer given O.\nThe O module produces output features by ï¬nding k supporting memories given x. They use k = 2. For k = 1 the highest scoring supporting memory is retrieved with:\no1 = O1(x, m) = arg max i=1,...,N sO(x, mi) (1)",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05698 | 53 | o1 = O1(x, m) = arg max i=1,...,N sO(x, mi) (1)
where sO is a function that scores the match between the pair of sentences x and mi. For the case k = 2 they then ï¬nd a second supporting memory given the ï¬rst found in the previous iteration:
o2 = O2(q, m) = arg max i=1,...,N where the candidate supporting memory mi is now scored with respect to both the original input and the ï¬rst supporting memory, where square brackets denote a list. The ï¬nal output o is [x, mo1, mo2], which is input to the module R.
Finally, R needs to produce a textual response r. While the authors also consider Recurrent Neural Networks (RNNs), their standard setup limits responses to be a single word (out of all the words seen by the model) by ranking them:
r = R(q, w) = argmaxwâW sR([x, mo1, mo2], w)
(3)
where W is the set of all words in the dictionary, and sR is a function that scores the match.
The scoring functions sO and sR have the same form, that of an embedding model: | 1502.05698#53 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 53,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "o1 = O1(x, m) = arg max i=1,...,N sO(x, mi) (1)\nwhere sO is a function that scores the match between the pair of sentences x and mi. For the case k = 2 they then ï¬nd a second supporting memory given the ï¬rst found in the previous iteration:\no2 = O2(q, m) = arg max i=1,...,N where the candidate supporting memory mi is now scored with respect to both the original input and the ï¬rst supporting memory, where square brackets denote a list. The ï¬nal output o is [x, mo1, mo2], which is input to the module R.\nFinally, R needs to produce a textual response r. While the authors also consider Recurrent Neural Networks (RNNs), their standard setup limits responses to be a single word (out of all the words seen by the model) by ranking them:\nr = R(q, w) = argmaxwâW sR([x, mo1, mo2], w)\n(3)\nwhere W is the set of all words in the dictionary, and sR is a function that scores the match.\nThe scoring functions sO and sR have the same form, that of an embedding model:",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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1502.05477 | 54 | The search direction is computed by approximately solving the equation Ax = g, where A is the Fisher information matrix, i.e., the quadratic approximation to the KL divergence constraint: DKL(θold, θ) â 1 2 (θ â θold)T A(θ â θold), where Aij = â DKL(θold, θ). In large-scale problems, it is prohibitively costly (with respect to computation and memory) to âθi form the full matrix A (or Aâ1). However, the conjugate gradient algorithm allows us to approximately solve the equation Ax = b without forming this full matrix, when we merely have access to a function that computes matrix-vector products y â Ay. Appendix C.1 describes the most efï¬cient way to compute matrix-vector products with the Fisher information matrix. For additional exposition on the use of Hessian-vector products for optimizing neural network objectives, see (Martens & Sutskever, 2012) and (Pascanu & Bengio, 2013). Having computed the search direction s â Aâ1g, we next need to compute the maximal step length β such that θ + | 1502.05477#54 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 54,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "The search direction is computed by approximately solving the equation Ax = g, where A is the Fisher information matrix, i.e., the quadratic approximation to the KL divergence constraint: DKL(θold, θ) â 1 2 (θ â θold)T A(θ â θold), where Aij = â DKL(θold, θ). In large-scale problems, it is prohibitively costly (with respect to computation and memory) to âθi form the full matrix A (or Aâ1). However, the conjugate gradient algorithm allows us to approximately solve the equation Ax = b without forming this full matrix, when we merely have access to a function that computes matrix-vector products y â Ay. Appendix C.1 describes the most efï¬cient way to compute matrix-vector products with the Fisher information matrix. For additional exposition on the use of Hessian-vector products for optimizing neural network objectives, see (Martens & Sutskever, 2012) and (Pascanu & Bengio, 2013). Having computed the search direction s â Aâ1g, we next need to compute the maximal step length β such that θ +",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 54 | The scoring functions sO and sR have the same form, that of an embedding model:
s(x, y) = Φx(x)â¤U â¤U Φy(y). (4)
where U is a n à D matrix where D is the number of features and n is the embedding dimension. The role of Φx and Φy is to map the original text to the D-dimensional feature space. They choose a bag of words representation, and D = 3|W | for sO, i.e., every word in the dictionary has three different representations: one for Φy(.) and two for Φx(.) depending on whether the words of the input arguments are from the actual input x or from the supporting memories so that they can be modeled differently. | 1502.05698#54 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 54,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "The scoring functions sO and sR have the same form, that of an embedding model:\ns(x, y) = Φx(x)â¤U â¤U Φy(y). (4)\nwhere U is a n à D matrix where D is the number of features and n is the embedding dimension. The role of Φx and Φy is to map the original text to the D-dimensional feature space. They choose a bag of words representation, and D = 3|W | for sO, i.e., every word in the dictionary has three different representations: one for Φy(.) and two for Φx(.) depending on whether the words of the input arguments are from the actual input x or from the supporting memories so that they can be modeled differently.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 55 | & Bengio, 2013). Having computed the search direction s â Aâ1g, we next need to compute the maximal step length β such that θ + βs will satisfy the KL divergence constraint. To do this, let δ = DKL â 1 2 β2sT As. From this, we obtain 2δ/sT As, where δ is the desired KL divergence. The term sT As can be computed through a single Hessian vector β = product, and it is also an intermediate result produced by the conjugate gradient algorithm. | 1502.05477#55 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 55,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "& Bengio, 2013). Having computed the search direction s â Aâ1g, we next need to compute the maximal step length β such that θ + βs will satisfy the KL divergence constraint. To do this, let δ = DKL â 1 2 β2sT As. From this, we obtain 2δ/sT As, where δ is the desired KL divergence. The term sT As can be computed through a single Hessian vector β = product, and it is also an intermediate result produced by the conjugate gradient algorithm.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 55 | They consider various extensions of their model, in particular modeling write time and modeling unseen words. Here we only discuss the former which we also use. In order for the model to work on QA tasks over stories it needs to know which order the sentences were uttered which is not available in the model directly. They thus add extra write time extra features to SO which take on the value 0 or 1 indicating which sentence is older than another being compared, and compare triples of pairs of sentences and the question itself. Training is carried out by stochastic gradient descent using supervision from both the question answer pairs and the supporting memories (to select o1 and o2). See Weston et al. (2014) for more details.
12
# Under review as a conference paper at ICLR 2016
A.1 SHORTCOMINGS OF THE EXISTING MEMNNS
The Memory Networks models deï¬ned in (Weston et al., 2014) are one possible technique to try on our tasks, however there are several tasks which they are likely to fail on:
⢠They model sentences with a bag of words so are likely to fail on tasks such as the 2- argument (task 4) and 3-argument (task 5) relation problems.
⢠They perform only two max operations (k = 2) so they cannot handle questions involving more than two supporting facts such as tasks 3 and 7. | 1502.05698#55 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 55,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "They consider various extensions of their model, in particular modeling write time and modeling unseen words. Here we only discuss the former which we also use. In order for the model to work on QA tasks over stories it needs to know which order the sentences were uttered which is not available in the model directly. They thus add extra write time extra features to SO which take on the value 0 or 1 indicating which sentence is older than another being compared, and compare triples of pairs of sentences and the question itself. Training is carried out by stochastic gradient descent using supervision from both the question answer pairs and the supporting memories (to select o1 and o2). See Weston et al. (2014) for more details.\n12\n# Under review as a conference paper at ICLR 2016\nA.1 SHORTCOMINGS OF THE EXISTING MEMNNS\nThe Memory Networks models deï¬ned in (Weston et al., 2014) are one possible technique to try on our tasks, however there are several tasks which they are likely to fail on:\n⢠They model sentences with a bag of words so are likely to fail on tasks such as the 2- argument (task 4) and 3-argument (task 5) relation problems.\n⢠They perform only two max operations (k = 2) so they cannot handle questions involving more than two supporting facts such as tasks 3 and 7.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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1502.05477 | 56 | Last, we use a line search to ensure improvement of the surrogate objective and satisfaction of the KL divergence constraint, both of which are nonlinear in the parameter vector θ (and thus depart from the linear and quadratic approximations used to compute the step). We perform the line search on the objective Lθold (θ) â X [DKL(θold, θ) ⤠δ], where X [. . . ] equals zero when its argument is true and +â when it is false. Starting with the maximal value of the step length β computed in the previous paragraph, we shrink β exponentially until the objective improves. Without this line search, the algorithm occasionally computes large steps that cause a catastrophic degradation of performance.
# C.1 Computing the Fisher-Vector Product
Here we will describe how to compute the matrix-vector product between the averaged Fisher information matrix and arbitrary vectors. This matrix-vector product enables us to perform the conjugate gradient algorithm. Suppose that the parameterized policy maps from the input x to âdistribution parameterâ vector µθ(x), which parameterizes the distribution Ï(u|x). Now the KL divergence for a given input x can be written as follows:
Dur (Moa (|) |] 7o(-)) = kl(uo (2), fora) (56) | 1502.05477#56 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 56,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "Last, we use a line search to ensure improvement of the surrogate objective and satisfaction of the KL divergence constraint, both of which are nonlinear in the parameter vector θ (and thus depart from the linear and quadratic approximations used to compute the step). We perform the line search on the objective Lθold (θ) â X [DKL(θold, θ) ⤠δ], where X [. . . ] equals zero when its argument is true and +â when it is false. Starting with the maximal value of the step length β computed in the previous paragraph, we shrink β exponentially until the objective improves. Without this line search, the algorithm occasionally computes large steps that cause a catastrophic degradation of performance.\n# C.1 Computing the Fisher-Vector Product\nHere we will describe how to compute the matrix-vector product between the averaged Fisher information matrix and arbitrary vectors. This matrix-vector product enables us to perform the conjugate gradient algorithm. Suppose that the parameterized policy maps from the input x to âdistribution parameterâ vector µθ(x), which parameterizes the distribution Ï(u|x). Now the KL divergence for a given input x can be written as follows:\nDur (Moa (|) |] 7o(-\u0002)) = kl(uo (2), fora) (56)",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 56 | ⢠They perform only two max operations (k = 2) so they cannot handle questions involving more than two supporting facts such as tasks 3 and 7.
⢠Unless a RNN is employed in the R module, they are unable to provide multiple answers in the standard setting using eq. (3). This is required for the list (8) and path ï¬nding (19) tasks.
We therefore propose improvements to their model in the following section.
IMPROVING MEMORY NETWORKS
A.2.1 ADAPTIVE MEMORIES (AND RESPONSES)
We consider a variable number of supporting facts that is automatically adapted dependent on the question being asked. To do this we consider scoring a special fact mâ
. Computation of supporting memories then becomes:
i = 1 oi = O(x, m) while oi 6= mâ
do i â i + 1 oi = O([x, mo1 , . . . , moiâ1 ], m) end while
That is, we keep predicting supporting facts i, conditioning at each step on the previously found facts, until mâ
is predicted at which point we stop. mâ
has its own unique embedding vector, which is also learned. In practice we still impose a hard maximum number of loops in our experiments to avoid fail cases where the computation never stops (in our experiments we use a limit of 10). | 1502.05698#56 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 56,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "⢠They perform only two max operations (k = 2) so they cannot handle questions involving more than two supporting facts such as tasks 3 and 7.\n⢠Unless a RNN is employed in the R module, they are unable to provide multiple answers in the standard setting using eq. (3). This is required for the list (8) and path ï¬nding (19) tasks.\nWe therefore propose improvements to their model in the following section.\nIMPROVING MEMORY NETWORKS\nA.2.1 ADAPTIVE MEMORIES (AND RESPONSES)\nWe consider a variable number of supporting facts that is automatically adapted dependent on the question being asked. To do this we consider scoring a special fact mâ
. Computation of supporting memories then becomes:\ni = 1 oi = O(x, m) while oi 6= mâ
do i â i + 1 oi = O([x, mo1 , . . . , moiâ1 ], m) end while\nThat is, we keep predicting supporting facts i, conditioning at each step on the previously found facts, until mâ
is predicted at which point we stop. mâ
has its own unique embedding vector, which is also learned. In practice we still impose a hard maximum number of loops in our experiments to avoid fail cases where the computation never stops (in our experiments we use a limit of 10).",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05698 | 57 | Multiple Answers We use a similar trick for the response module as well in order to output multi- ple words. That is, we add a special word wâ
to the dictionary and predict word wi on each iteration i conditional on the previous words, i.e., wi = R([x, mo1 , . . . , m|o|, wi, . . . , wiâ1], w), until we predict wâ
.
A.2.2 NONLINEAR SENTENCE MODELING
There are several ways of modeling sentences that go beyond a bag-of-words, and we explore three variants here. The simplest is a bag-of-N -grams, we consider N = 1, 2 and 3 in the bag. The main disadvantage of such a method is that the dictionary grows rapidly with N . We therefore consider an alternative neural network approach, which we call a multilinear map. Each word in a sentence is binned into one of Psz positions with p(i, l) = â(iPsz)/l)â where i is the position of the word in a sentence of length l, and for each position we employ a n à n matrix Pp(i,l). We then model the matching score with: | 1502.05698#57 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 57,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Multiple Answers We use a similar trick for the response module as well in order to output multi- ple words. That is, we add a special word wâ
to the dictionary and predict word wi on each iteration i conditional on the previous words, i.e., wi = R([x, mo1 , . . . , m|o|, wi, . . . , wiâ1], w), until we predict wâ
.\nA.2.2 NONLINEAR SENTENCE MODELING\nThere are several ways of modeling sentences that go beyond a bag-of-words, and we explore three variants here. The simplest is a bag-of-N -grams, we consider N = 1, 2 and 3 in the bag. The main disadvantage of such a method is that the dictionary grows rapidly with N . We therefore consider an alternative neural network approach, which we call a multilinear map. Each word in a sentence is binned into one of Psz positions with p(i, l) = â(iPsz)/l)â where i is the position of the word in a sentence of length l, and for each position we employ a n à n matrix Pp(i,l). We then model the matching score with:",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
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1502.05477 | 58 | where the primes (â) indicate differentiation with respect to the first argument, and there is an implied summation over indices a,b. The second term vanishes, leaving just the first term. Let J := Peat) (the Jacobian), then the Fisher information matrix can be written in matrix form as Jâ MJ, where M = kl!!,(j19(x), Hoa) is the Fisher information matrix of the distribution in terms of the mean parameter ju (as opposed to the parameter @). This has a simple form for most parameterized distributions of interest. The Fisher-vector product can now be written as a function y + J7M.Jy. Multiplication by J7 and J can be performed by most automatic differentiation and neural network packages (multiplication by J7 is the well-known backprop operation), and the operation for multiplication by MM can be derived for the distribution of interest. Note that this Fisher-vector product is straightforward to average over a set of datapoints, i.e., inputs x to pu. | 1502.05477#58 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 58,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "where the primes (â) indicate differentiation with respect to the first argument, and there is an implied summation over indices a,b. The second term vanishes, leaving just the first term. Let J := Peat) (the Jacobian), then the Fisher information matrix can be written in matrix form as Jâ MJ, where M = kl!!,(j19(x), Hoa) is the Fisher information matrix of the distribution in terms of the mean parameter ju (as opposed to the parameter @). This has a simple form for most parameterized distributions of interest. The Fisher-vector product can now be written as a function y + J7M.Jy. Multiplication by J7 and J can be performed by most automatic differentiation and neural network packages (multiplication by J7 is the well-known backprop operation), and the operation for multiplication by MM can be derived for the distribution of interest. Note that this Fisher-vector product is straightforward to average over a set of datapoints, i.e., inputs x to pu.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 58 | s(q, d) = E(q) · E(d); E(x) = tanh( X Pp(i,l)Φx(xi)â¤U ) i=1,...,l (5)
whereby we apply a linear map for each word dependent on its position, followed by a tanh non- linearity on the sum of mappings. Note that this is related to the model of (Yu et al., 2014) who consider tags rather than positions. While the results of this method are not shown in the main paper due to space restrictions, it performs similarly well to N -grams to and may be useful in real-world cases where N -grams cause the dictionary to be too large. Comparing to Table 3 MemNN with adaptive memories (AM) + multilinear obtains a mean performance of 93, the same as MemNNs with AM+NG+NL (i.e., using N-grams instead).
13
# Under review as a conference paper at ICLR 2016
Finally, to assess the performance of nonlinear maps that do not model word position at all we also consider the following nonlinear embedding: | 1502.05698#58 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 58,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "s(q, d) = E(q) · E(d); E(x) = tanh( X Pp(i,l)Φx(xi)â¤U ) i=1,...,l (5)\nwhereby we apply a linear map for each word dependent on its position, followed by a tanh non- linearity on the sum of mappings. Note that this is related to the model of (Yu et al., 2014) who consider tags rather than positions. While the results of this method are not shown in the main paper due to space restrictions, it performs similarly well to N -grams to and may be useful in real-world cases where N -grams cause the dictionary to be too large. Comparing to Table 3 MemNN with adaptive memories (AM) + multilinear obtains a mean performance of 93, the same as MemNNs with AM+NG+NL (i.e., using N-grams instead).\n13\n# Under review as a conference paper at ICLR 2016\nFinally, to assess the performance of nonlinear maps that do not model word position at all we also consider the following nonlinear embedding:",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 59 | One could alternatively use a generic method for calculating Hessian-vector products using reverse mode automatic differ- entiation ((Wright & Nocedal, 1999), chapter 8), computing the Hessian of DKL with respect to θ. This method would be slightly less efï¬cient as it does not exploit the fact that the second derivatives of µ(x) (i.e., the second term in Equation (57)) can be ignored, but may be substantially easier to implement.
We have described a procedure for computing the Fisher-vector product y â Ay, where the Fisher information matrix is averaged over a set of inputs to the function µ. Computing the Fisher-vector product is typically about as expensive as computing the gradient of an objective that depends on µ(x) (Wright & Nocedal, 1999). Furthermore, we need to compute
Trust Region Policy Optimization | 1502.05477#59 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 59,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "One could alternatively use a generic method for calculating Hessian-vector products using reverse mode automatic differ- entiation ((Wright & Nocedal, 1999), chapter 8), computing the Hessian of DKL with respect to θ. This method would be slightly less efï¬cient as it does not exploit the fact that the second derivatives of µ(x) (i.e., the second term in Equation (57)) can be ignored, but may be substantially easier to implement.\nWe have described a procedure for computing the Fisher-vector product y â Ay, where the Fisher information matrix is averaged over a set of inputs to the function µ. Computing the Fisher-vector product is typically about as expensive as computing the gradient of an objective that depends on µ(x) (Wright & Nocedal, 1999). Furthermore, we need to compute\nTrust Region Policy Optimization",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 59 | # Under review as a conference paper at ICLR 2016
Finally, to assess the performance of nonlinear maps that do not model word position at all we also consider the following nonlinear embedding:
E(x) = tanh(W tanh(Φx(x)â¤U )). (6) where W is a n à n matrix. This is similar to a classical two-layer neural network, but applied to both sides q and d of s(q, d). We also consider the straight-forward combination of bag-of-N -grams followed by this nonlinearity.
# B BASELINE USING EXTERNAL RESOURCES
We also built a classical cascade NLP system baseline using a structured SVM, which incorpo- rates coreference resolution and semantic role labeling preprocessing steps, which are themselves trained on large amounts of costly labeled data. We ï¬rst run the Stanford coreference system (Raghunathan et al., 2010) on the stories and each mention is then replaced with the ï¬rst mention of its entity class. Second, the SENNA semantic role labeling system (SRL) (Collobert et al., 2011) is run, and we collect the set of arguments for each verb. We then deï¬ne a ranking task for ï¬nding the supporting facts (trained using strong supervision): | 1502.05698#59 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 59,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "# Under review as a conference paper at ICLR 2016\nFinally, to assess the performance of nonlinear maps that do not model word position at all we also consider the following nonlinear embedding:\nE(x) = tanh(W tanh(Φx(x)â¤U )). (6) where W is a n à n matrix. This is similar to a classical two-layer neural network, but applied to both sides q and d of s(q, d). We also consider the straight-forward combination of bag-of-N -grams followed by this nonlinearity.\n# B BASELINE USING EXTERNAL RESOURCES\nWe also built a classical cascade NLP system baseline using a structured SVM, which incorpo- rates coreference resolution and semantic role labeling preprocessing steps, which are themselves trained on large amounts of costly labeled data. We ï¬rst run the Stanford coreference system (Raghunathan et al., 2010) on the stories and each mention is then replaced with the ï¬rst mention of its entity class. Second, the SENNA semantic role labeling system (SRL) (Collobert et al., 2011) is run, and we collect the set of arguments for each verb. We then deï¬ne a ranking task for ï¬nding the supporting facts (trained using strong supervision):",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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1502.05477 | 60 | Trust Region Policy Optimization
k of these Fisher-vector products per gradient, where k is the number of iterations of the conjugate gradient algorithm we perform. We found k = 10 to be quite effective, and using higher k did not result in faster policy improvement. Hence, a na¨ıve implementation would spend more than 90% of the computational effort on these Fisher-vector products. However, we can greatly reduce this burden by subsampling the data for the computation of Fisher-vector product. Since the Fisher information matrix merely acts as a metric, it can be computed on a subset of the data without severely degrading the quality of the ï¬nal step. Hence, we can compute it on 10% of the data, and the total cost of Hessian-vector products will be about the same as computing the gradient. With this optimization, the computation of a natural gradient step Aâ1g does not incur a signiï¬cant extra computational cost beyond computing the gradient g.
# D Approximating Factored Policies with Neural Networks | 1502.05477#60 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 60,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "Trust Region Policy Optimization\nk of these Fisher-vector products per gradient, where k is the number of iterations of the conjugate gradient algorithm we perform. We found k = 10 to be quite effective, and using higher k did not result in faster policy improvement. Hence, a na¨ıve implementation would spend more than 90% of the computational effort on these Fisher-vector products. However, we can greatly reduce this burden by subsampling the data for the computation of Fisher-vector product. Since the Fisher information matrix merely acts as a metric, it can be computed on a subset of the data without severely degrading the quality of the ï¬nal step. Hence, we can compute it on 10% of the data, and the total cost of Hessian-vector products will be about the same as computing the gradient. With this optimization, the computation of a natural gradient step Aâ1g does not incur a signiï¬cant extra computational cost beyond computing the gradient g.\n# D Approximating Factored Policies with Neural Networks",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05477 | 61 | # D Approximating Factored Policies with Neural Networks
The policy, which is a conditional probability distribution Ïθ(a|s), can be parameterized with a neural network. This neural network maps (deterministically) from the state vector s to a vector µ, which speciï¬es a distribution over action space. Then we can compute the likelihood p(a|µ) and sample a â¼ p(a|µ).
For our experiments with continuous state and action spaces, we used a Gaussian distribution, where the covariance matrix was diagonal and independent of the state. A neural network with several fully-connected (dense) layers maps from the input features to the mean of a Gaussian distribution. A separate set of parameters speciï¬es the log standard deviation of each element. More concretely, the parameters include a set of weights and biases for the neural network computing the mean, {Wi, bi}L i=1, and a vector r (log standard deviation) with the same dimension as a. Then, the policy is deï¬ned by the normal distribution N | 1502.05477#61 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 61,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "# D Approximating Factored Policies with Neural Networks\nThe policy, which is a conditional probability distribution Ïθ(a|s), can be parameterized with a neural network. This neural network maps (deterministically) from the state vector s to a vector µ, which speciï¬es a distribution over action space. Then we can compute the likelihood p(a|µ) and sample a â¼ p(a|µ).\nFor our experiments with continuous state and action spaces, we used a Gaussian distribution, where the covariance matrix was diagonal and independent of the state. A neural network with several fully-connected (dense) layers maps from the input features to the mean of a Gaussian distribution. A separate set of parameters speciï¬es the log standard deviation of each element. More concretely, the parameters include a set of weights and biases for the neural network computing the mean, {Wi, bi}L i=1, and a vector r (log standard deviation) with the same dimension as a. Then, the policy is deï¬ned by the normal distribution N",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 61 | where given the question x we ï¬nd at most three supporting facts with indices oi from the set of facts f in the story (we also consider selecting an âempty factâ for the case of less than three), and SO is a linear scoring function with parameters Î. Computing the argmax requires doing exhaustive search, unlike e.g. the MemNN method which is greedy. For scalability, we thus prune the set of possible matches by requiring that facts share one common non-determiner word with each other match or with x. SO is constructed as a set of indicator features. For simplicity each of the features only looks at pairs of sentences, i.e. SO(x, fo1, fo2, fo3; Î) = Î â (g(x, fo1 ), g(x, fo2), g(x, fo3 ), g(fo1, fo2), g(fo2, fo3), g(fo1, fo3)). The feature function g is made up of the following feature types, shown here for g(fo1, fo2): (1) Word pairs: One indicator variable for each pair of words in fo1 and fo2. (2) Pair distance: Indicator for the distance between the sentence, i.e. | 1502.05698#61 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 61,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "where given the question x we ï¬nd at most three supporting facts with indices oi from the set of facts f in the story (we also consider selecting an âempty factâ for the case of less than three), and SO is a linear scoring function with parameters Î. Computing the argmax requires doing exhaustive search, unlike e.g. the MemNN method which is greedy. For scalability, we thus prune the set of possible matches by requiring that facts share one common non-determiner word with each other match or with x. SO is constructed as a set of indicator features. For simplicity each of the features only looks at pairs of sentences, i.e. SO(x, fo1, fo2, fo3; Î) = Î â (g(x, fo1 ), g(x, fo2), g(x, fo3 ), g(fo1, fo2), g(fo2, fo3), g(fo1, fo3)). The feature function g is made up of the following feature types, shown here for g(fo1, fo2): (1) Word pairs: One indicator variable for each pair of words in fo1 and fo2. (2) Pair distance: Indicator for the distance between the sentence, i.e.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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1502.05477 | 62 | For the experiments with discrete actions (Atari), we use a factored discrete action space, where each factor is parameter- ized as a categorical distribution. That is, the action consists of a tuple (a1, a2,...,a«) of integers a, ⬠{1,2,..., Nx}, and each of these components is assumed to have a categorical distribution, which is specified by a vector uw, = [p1,P2,---;PN,]. Hence, ps is defined to be the concatenation of the factorsâ parameters: pp = [/11, Ju2,---, 4K] and has dimension dim pz = ian N;,. The components of j are computed by taking applying a neural network to the input s and then applying the softmax operator to each slice, yielding normalized probabilities for each factor.
# E Experiment Parameters | 1502.05477#62 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 62,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "For the experiments with discrete actions (Atari), we use a factored discrete action space, where each factor is parameter- ized as a categorical distribution. That is, the action consists of a tuple (a1, a2,...,a«) of integers a, ⬠{1,2,..., Nx}, and each of these components is assumed to have a categorical distribution, which is specified by a vector uw, = [p1,P2,---;PN,]. Hence, ps is defined to be the concatenation of the factorsâ parameters: pp = [/11, Ju2,---, 4K] and has dimension dim pz = ian N;,. The components of j are computed by taking applying a neural network to the input s and then applying the softmax operator to each slice, yielding normalized probabilities for each factor.\n# E Experiment Parameters",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 62 | pairs: One indicator variable for each pair of words in fo1 and fo2. (2) Pair distance: Indicator for the distance between the sentence, i.e. o1 â o2. (3) Pair order: Indicator for the order of the sentence, i.e. o1 > o2. (4) SRL Verb Pair: Indicator variables for each pair of SRL verbs in fo1 and fo2. (5) SRL Verb-Arg Pair: Indicator variables for each pair of SRL arguments in fo1, fo2 and their corresponding verbs. After ï¬nding the supporting facts, we build a similar structured SVM for the response stage, also with features tuned for that goal: Words â indicator for each word in x, Word Pairs â indicator for each pair of words in x and supporting facts, and similar SRL Verb and SRL Verb-Arg Pair features as before. | 1502.05698#62 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 62,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "pairs: One indicator variable for each pair of words in fo1 and fo2. (2) Pair distance: Indicator for the distance between the sentence, i.e. o1 â o2. (3) Pair order: Indicator for the order of the sentence, i.e. o1 > o2. (4) SRL Verb Pair: Indicator variables for each pair of SRL verbs in fo1 and fo2. (5) SRL Verb-Arg Pair: Indicator variables for each pair of SRL arguments in fo1, fo2 and their corresponding verbs. After ï¬nding the supporting facts, we build a similar structured SVM for the response stage, also with features tuned for that goal: Words â indicator for each word in x, Word Pairs â indicator for each pair of words in x and supporting facts, and similar SRL Verb and SRL Verb-Arg Pair features as before.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 63 | # E Experiment Parameters
Swimmer Hopper Walker State space dim. Control space dim. Total num. policy params Sim. steps per iter. Policy iter. Stepsize (DKL) Hidden layer size Discount (γ) Vine: rollout length Vine: rollouts per state Vine: Q-values per batch Vine: num. rollouts for sampling Vine: len. rollouts for sampling Vine: computation time (minutes) SP: num. path SP: path len. SP: computation time 10 2 364 50K 200 0.01 30 0.99 50 4 500 16 1000 2 50 1000 5 12 3 4806 1M 200 0.01 50 0.99 100 4 2500 16 1000 14 1000 1000 35 20 6 8206 1M 200 0.01 50 0.99 100 4 2500 16 1000 40 10000 1000 100
Table 2. Parameters for continuous control tasks, vine and single path (SP) algorithms.
Trust Region Policy Optimization
Total num. policy params Vine: Sim. steps per iter. SP: Sim. steps per iter. Policy iter. Stepsize (DKL) Discount (γ) Vine: rollouts per state Vine: computation time SP: computation time All games 33500 400K 100K 500 0.01 0.99 â 4 â 30 hrs â 30 hrs
Table 3. Parameters used for Atari domain.
# F Learning Curves for the Atari Domain | 1502.05477#63 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 63,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "# E Experiment Parameters\nSwimmer Hopper Walker State space dim. Control space dim. Total num. policy params Sim. steps per iter. Policy iter. Stepsize (DKL) Hidden layer size Discount (γ) Vine: rollout length Vine: rollouts per state Vine: Q-values per batch Vine: num. rollouts for sampling Vine: len. rollouts for sampling Vine: computation time (minutes) SP: num. path SP: path len. SP: computation time 10 2 364 50K 200 0.01 30 0.99 50 4 500 16 1000 2 50 1000 5 12 3 4806 1M 200 0.01 50 0.99 100 4 2500 16 1000 14 1000 1000 35 20 6 8206 1M 200 0.01 50 0.99 100 4 2500 16 1000 40 10000 1000 100\nTable 2. Parameters for continuous control tasks, vine and single path (SP) algorithms.\nTrust Region Policy Optimization\nTotal num. policy params Vine: Sim. steps per iter. SP: Sim. steps per iter. Policy iter. Stepsize (DKL) Discount (γ) Vine: rollouts per state Vine: computation time SP: computation time All games 33500 400K 100K 500 0.01 0.99 â 4 â 30 hrs â 30 hrs\nTable 3. Parameters used for Atari domain.\n# F Learning Curves for the Atari Domain",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 63 | Results are given in Table 3. The structured SVM, despite having access to external resources, does It does perform well on tasks not perform better than MemNNs overall, still failing at 9 tasks. 6, 9 and 10where the hand-built feature conjunctions capture the necessary nonlinearities that the original MemNNs do not. However, it seems to do signiï¬cantly worse on tasks requiring three (and sometimes, two) supporting facts (e.g. tasks 3, 16 and 2) presumably as ranking over so many possibilities introduces more mistakes. However, its non-greedy search does seem to help on other tasks, such as path ï¬nding (task 19) where search is very important.
14 | 1502.05698#63 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 63,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Results are given in Table 3. The structured SVM, despite having access to external resources, does It does perform well on tasks not perform better than MemNNs overall, still failing at 9 tasks. 6, 9 and 10where the hand-built feature conjunctions capture the necessary nonlinearities that the original MemNNs do not. However, it seems to do signiï¬cantly worse on tasks requiring three (and sometimes, two) supporting facts (e.g. tasks 3, 16 and 2) presumably as ranking over so many possibilities introduces more mistakes. However, its non-greedy search does seem to help on other tasks, such as path ï¬nding (task 19) where search is very important.\n14",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
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] |
1502.05477 | 64 | cost cost 406 666 | .3 860 }.......8 beam rider >| â single path 1008 |e ccehe 1200 |Beeeeeemmeetnnst ecenemc mecca 1400 |occcccccceccceschevscescsevscevegecscecesecseceesgecseserecesseeva}ecsees 1666 | 8 30, 166 266 366 406 number of policy iterations pong â_â single path 568 100 200 300 400 number of policy iterations cost breakout â single path / â vine | cost to) 100 200 300 number of policy iterations 6 . qbert : : â single path -1006 }-.--. spoced feccescesaRtiea. ans â vine -2000 as . : 3000 b..--eeeeeeeeee ee ee. co Pacaooccnoasooocs reaccoesonsocccoctouooccocnosecno! ~ S -4e08 é. v . âSOOO rccnsececc en seettensccareneneed fecteeeeeeed Orr -6666 weds : . i . yi -7660 | sceoecosocg0s0d bosoecacsoocoISH pcocooSnongeDoCabSocasoISEORaIEHO! Boot 8668 bo a 166 266 366 406 566 number of policy | 1502.05477#64 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 64,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "cost cost 406 666 | .3 860 }.......8 beam rider >| â single path 1008 |e ccehe 1200 |Beeeeeemmeetnnst ecenemc mecca 1400 |occcccccceccceschevscescsevscevegecscecesecseceesgecseserecesseeva}ecsees 1666 | 8 30, 166 266 366 406 number of policy iterations pong â_â single path 568 100 200 300 400 number of policy iterations cost breakout â single path / â vine | cost to) 100 200 300 number of policy iterations 6 . qbert : : â single path -1006 }-.--. spoced feccescesaRtiea. ans â vine -2000 as . : 3000 b..--eeeeeeeeee ee ee. co Pacaooccnoasooocs reaccoesonsocccoctouooccocnosecno! ~ S -4e08 é. v . âSOOO rccnsececc en seettensccareneneed fecteeeeeeed Orr -6666 weds : . i . yi -7660 | sceoecosocg0s0d bosoecacsoocoISH pcocooSnongeDoCabSocasoISEORaIEHO! Boot 8668 bo a 166 266 366 406 566 number of policy",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05477 | 65 | bosoecacsoocoISH pcocooSnongeDoCabSocasoISEORaIEHO! Boot 8668 bo a 166 266 366 406 566 number of policy iterations 100 pe space invaders 0 â. : : : â single path 6 100 200 368 406 number of policy iterations cost enduro : â single path -~600 2 : 2 : 100 200 300 400 500 number of policy iterations 500 seaquest : : â single path > â= vine 6 -500 [eens SC boccoccecuccoccrtocooconcoccoet, number of policy iterations | 1502.05477#65 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 65,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "bosoecacsoocoISH pcocooSnongeDoCabSocasoISEORaIEHO! Boot 8668 bo a 166 266 366 406 566 number of policy iterations 100 pe space invaders 0 â. : : : â single path 6 100 200 368 406 number of policy iterations cost enduro : â single path -~600 2 : 2 : 100 200 300 400 500 number of policy iterations 500 seaquest : : â single path > â= vine 6 -500 [eens SC boccoccecuccoccrtocooconcoccoet, number of policy iterations",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.04623 | 0 | # DRAW: A Recurrent Neural Network For Image Generation
Karol Gregor Ivo Danihelka Alex Graves Danilo Jimenez Rezende Daan Wierstra Google DeepMind
Google DeepMind
5 1 0 2
y a M 0 2 ] V C . s c [
# Abstract
This paper introduces the Deep Recurrent Atten- tive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images. The system substantially improves on the state of the art for generative models on MNIST, and, when trained on the Street View House Numbers dataset, it generates images that cannot be distin- guished from real data with the naked eye.
2 v 3 2 6 4 0 . 2 0 5 1 : v i X r a
# 1. Introduction | 1502.04623#0 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 0,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "# DRAW: A Recurrent Neural Network For Image Generation\nKarol Gregor Ivo Danihelka Alex Graves Danilo Jimenez Rezende Daan Wierstra Google DeepMind\nGoogle DeepMind\n5 1 0 2\ny a M 0 2 ] V C . s c [\n# Abstract\nThis paper introduces the Deep Recurrent Atten- tive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images. The system substantially improves on the state of the art for generative models on MNIST, and, when trained on the Street View House Numbers dataset, it generates images that cannot be distin- guished from real data with the naked eye.\n2 v 3 2 6 4 0 . 2 0 5 1 : v i X r a\n# 1. Introduction",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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1502.04623 | 1 | 2 v 3 2 6 4 0 . 2 0 5 1 : v i X r a
# 1. Introduction
A person asked to draw, paint or otherwise recreate a visual scene will naturally do so in a sequential, iterative fashion, reassessing their handiwork after each modiï¬cation. Rough outlines are gradually replaced by precise forms, lines are sharpened, darkened or erased, shapes are altered, and the ï¬nal picture emerges. Most approaches to automatic im- age generation, however, aim to generate entire scenes at once. In the context of generative neural networks, this typ- ically means that all the pixels are conditioned on a single latent distribution (Dayan et al., 1995; Hinton & Salakhut- dinov, 2006; Larochelle & Murray, 2011). As well as pre- cluding the possibility of iterative self-correction, the âone shotâ approach is fundamentally difï¬cult to scale to large images. The Deep Recurrent Attentive Writer (DRAW) ar- chitecture represents a shift towards a more natural form of image construction, in which parts of a scene are created independently from others, and approximate sketches are successively reï¬ned. | 1502.04623#1 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 1,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "2 v 3 2 6 4 0 . 2 0 5 1 : v i X r a\n# 1. Introduction\nA person asked to draw, paint or otherwise recreate a visual scene will naturally do so in a sequential, iterative fashion, reassessing their handiwork after each modiï¬cation. Rough outlines are gradually replaced by precise forms, lines are sharpened, darkened or erased, shapes are altered, and the ï¬nal picture emerges. Most approaches to automatic im- age generation, however, aim to generate entire scenes at once. In the context of generative neural networks, this typ- ically means that all the pixels are conditioned on a single latent distribution (Dayan et al., 1995; Hinton & Salakhut- dinov, 2006; Larochelle & Murray, 2011). As well as pre- cluding the possibility of iterative self-correction, the âone shotâ approach is fundamentally difï¬cult to scale to large images. The Deep Recurrent Attentive Writer (DRAW) ar- chitecture represents a shift towards a more natural form of image construction, in which parts of a scene are created independently from others, and approximate sketches are successively reï¬ned.",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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1502.04623 | 3 | Time â~
Figure 1. A trained DRAW network generating MNIST dig- its. Each row shows successive stages in the generation of a sin- gle digit. Note how the lines composing the digits appear to be âdrawnâ by the network. The red rectangle delimits the area at- tended to by the network at each time-step, with the focal preci- sion indicated by the width of the rectangle border.
The core of the DRAW architecture is a pair of recurrent neural networks: an encoder network that compresses the real images presented during training, and a decoder that reconstitutes images after receiving codes. The combined system is trained end-to-end with stochastic gradient de- scent, where the loss function is a variational upper bound on the log-likelihood of the data. It therefore belongs to the family of variational auto-encoders, a recently emerged hybrid of deep learning and variational inference that has led to signiï¬cant advances in generative modelling (Gre- gor et al., 2014; Kingma & Welling, 2014; Rezende et al., 2014; Mnih & Gregor, 2014; Salimans et al., 2014). Where DRAW differs from its siblings is that, rather than generatDRAW: A Recurrent Neural Network For Image Generation | 1502.04623#3 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 3,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "Time â~\nFigure 1. A trained DRAW network generating MNIST dig- its. Each row shows successive stages in the generation of a sin- gle digit. Note how the lines composing the digits appear to be âdrawnâ by the network. The red rectangle delimits the area at- tended to by the network at each time-step, with the focal preci- sion indicated by the width of the rectangle border.\nThe core of the DRAW architecture is a pair of recurrent neural networks: an encoder network that compresses the real images presented during training, and a decoder that reconstitutes images after receiving codes. The combined system is trained end-to-end with stochastic gradient de- scent, where the loss function is a variational upper bound on the log-likelihood of the data. It therefore belongs to the family of variational auto-encoders, a recently emerged hybrid of deep learning and variational inference that has led to signiï¬cant advances in generative modelling (Gre- gor et al., 2014; Kingma & Welling, 2014; Rezende et al., 2014; Mnih & Gregor, 2014; Salimans et al., 2014). Where DRAW differs from its siblings is that, rather than generatDRAW: A Recurrent Neural Network For Image Generation",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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1502.04623 | 4 | ing images in a single pass, it iteratively constructs scenes through an accumulation of modiï¬cations emitted by the decoder, each of which is observed by the encoder.
An obvious correlate of generating images step by step is the ability to selectively attend to parts of the scene while ignoring others. A wealth of results in the past few years suggest that visual structure can be better captured by a se- quence of partial glimpses, or foveations, than by a sin- gle sweep through the entire image (Larochelle & Hinton, 2010; Denil et al., 2012; Tang et al., 2013; Ranzato, 2014; Zheng et al., 2014; Mnih et al., 2014; Ba et al., 2014; Ser- manet et al., 2014). The main challenge faced by sequential attention models is learning where to look, which can be addressed with reinforcement learning techniques such as policy gradients (Mnih et al., 2014). The attention model in DRAW, however, is fully differentiable, making it possible to train with standard backpropagation. In this sense it re- sembles the selective read and write operations developed for the Neural Turing Machine (Graves et al., 2014). | 1502.04623#4 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 4,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "ing images in a single pass, it iteratively constructs scenes through an accumulation of modiï¬cations emitted by the decoder, each of which is observed by the encoder.\nAn obvious correlate of generating images step by step is the ability to selectively attend to parts of the scene while ignoring others. A wealth of results in the past few years suggest that visual structure can be better captured by a se- quence of partial glimpses, or foveations, than by a sin- gle sweep through the entire image (Larochelle & Hinton, 2010; Denil et al., 2012; Tang et al., 2013; Ranzato, 2014; Zheng et al., 2014; Mnih et al., 2014; Ba et al., 2014; Ser- manet et al., 2014). The main challenge faced by sequential attention models is learning where to look, which can be addressed with reinforcement learning techniques such as policy gradients (Mnih et al., 2014). The attention model in DRAW, however, is fully differentiable, making it possible to train with standard backpropagation. In this sense it re- sembles the selective read and write operations developed for the Neural Turing Machine (Graves et al., 2014).",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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1502.04623 | 5 | The following section deï¬nes the DRAW architecture, along with the loss function used for training and the pro- cedure for image generation. Section 3 presents the selec- tive attention model and shows how it is applied to read- ing and modifying images. Section 4 provides experi- mental results on the MNIST, Street View House Num- bers and CIFAR-10 datasets, with examples of generated images; and concluding remarks are given in Section 5. Lastly, we would like to direct the reader to the video accompanying this paper (https://www.youtube. com/watch?v=Zt-7MI9eKEo) which contains exam- ples of DRAW networks reading and generating images.
P(x\|z) Cyâ-1â+[write -+Crâ[write >... ~erâ-[o}-P(2| 21-7) t TL \ 7 decoder dec_,[decoder| _|_ [decoder FNN fe RN "LRNN [~~ t \ | t il 4 ae decoding [sample] sample] \ | [sample T 7 i encoding Q(|x) Q(etl@, Z12- ) inference) \ (Cea Es Z1:t { h ene Â¥ encoder âencoder Ly âencoder RNN \ T ear) ia t I I | 1502.04623#5 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 5,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "The following section deï¬nes the DRAW architecture, along with the loss function used for training and the pro- cedure for image generation. Section 3 presents the selec- tive attention model and shows how it is applied to read- ing and modifying images. Section 4 provides experi- mental results on the MNIST, Street View House Num- bers and CIFAR-10 datasets, with examples of generated images; and concluding remarks are given in Section 5. Lastly, we would like to direct the reader to the video accompanying this paper (https://www.youtube. com/watch?v=Zt-7MI9eKEo) which contains exam- ples of DRAW networks reading and generating images.\nP(x\\|z) Cyâ-1â+[write -+Crâ[write >... ~erâ-[o}-P(2| 21-7) t TL \\ 7 decoder dec_,[decoder| _|_ [decoder FNN fe RN \"LRNN [~~ t \\ | t il 4 ae decoding [sample] sample] \\ | [sample T 7 i encoding Q(|x) Q(etl@, Z12- ) inference) \\ (Cea Es Z1:t { h ene Â¥ encoder âencoder Ly âencoder RNN \\ T ear) ia t I I",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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] |
1502.04623 | 6 | Figure 2. Left: Conventional Variational Auto-Encoder. Dur- ing generation, a sample z is drawn from a prior P(z) and passed through the feedforward decoder network to compute the proba- bility of the input P(x|z) given the sample. During inference the input x is passed to the encoder network, producing an approx- imate posterior Q(z|x) over latent variables. During training, z is sampled from Q(z|x) and then used to compute the total de- scription length KL(Q(2Z|x)||P(Z)) â log(P(2|z)), which is minimised with stochastic gradient descent. Right: DRAW Net- work. At each time-step a sample z; from the prior P(z,) is passed to the recurrent decoder network, which then modifies part of the canvas matrix. The final canvas matrix cr is used to com- pute P(x|z1.7). During inference the input is read at every time- step and the result is passed to the encoder RNN. The RNNs at the previous time-step specify where to read. The output of the encoder RNN is used to compute the approximate posterior over the latent variables at that time-step. | 1502.04623#6 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 6,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "Figure 2. Left: Conventional Variational Auto-Encoder. Dur- ing generation, a sample z is drawn from a prior P(z) and passed through the feedforward decoder network to compute the proba- bility of the input P(x|z) given the sample. During inference the input x is passed to the encoder network, producing an approx- imate posterior Q(z|x) over latent variables. During training, z is sampled from Q(z|x) and then used to compute the total de- scription length KL(Q(2Z|x)||P(Z)) â log(P(2|z)), which is minimised with stochastic gradient descent. Right: DRAW Net- work. At each time-step a sample z; from the prior P(z,) is passed to the recurrent decoder network, which then modifies part of the canvas matrix. The final canvas matrix cr is used to com- pute P(x|z1.7). During inference the input is read at every time- step and the result is passed to the encoder RNN. The RNNs at the previous time-step specify where to read. The output of the encoder RNN is used to compute the approximate posterior over the latent variables at that time-step.",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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1502.04623 | 7 | as âwhat to writeâ. The architecture is sketched in Fig. 2, alongside a feedforward variational auto-encoder.
# 2.1. Network Architecture
# 2. The DRAW Network
The basic structure of a DRAW network is similar to that of other variational auto-encoders: an encoder network deter- mines a distribution over latent codes that capture salient information about the input data; a decoder network re- ceives samples from the code distribuion and uses them to condition its own distribution over images. However there are three key differences. Firstly, both the encoder and de- coder are recurrent networks in DRAW, so that a sequence of code samples is exchanged between them; moreover the encoder is privy to the decoderâs previous outputs, allow- ing it to tailor the codes it sends according to the decoderâs behaviour so far. Secondly, the decoderâs outputs are suc- cessively added to the distribution that will ultimately gen- erate the data, as opposed to emitting this distribution in a single step. And thirdly, a dynamically updated atten- tion mechanism is used to restrict both the input region observed by the encoder, and the output region modiï¬ed by the decoder. In simple terms, the network decides at each time-step âwhere to readâ and âwhere to writeâ as well | 1502.04623#7 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 7,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "as âwhat to writeâ. The architecture is sketched in Fig. 2, alongside a feedforward variational auto-encoder.\n# 2.1. Network Architecture\n# 2. The DRAW Network\nThe basic structure of a DRAW network is similar to that of other variational auto-encoders: an encoder network deter- mines a distribution over latent codes that capture salient information about the input data; a decoder network re- ceives samples from the code distribuion and uses them to condition its own distribution over images. However there are three key differences. Firstly, both the encoder and de- coder are recurrent networks in DRAW, so that a sequence of code samples is exchanged between them; moreover the encoder is privy to the decoderâs previous outputs, allow- ing it to tailor the codes it sends according to the decoderâs behaviour so far. Secondly, the decoderâs outputs are suc- cessively added to the distribution that will ultimately gen- erate the data, as opposed to emitting this distribution in a single step. And thirdly, a dynamically updated atten- tion mechanism is used to restrict both the input region observed by the encoder, and the output region modiï¬ed by the decoder. In simple terms, the network decides at each time-step âwhere to readâ and âwhere to writeâ as well",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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] |
1502.04623 | 8 | Let RNN enc be the function enacted by the encoder net- work at a single time-step. The output of RNN enc at time t is the encoder hidden vector henc . Similarly the output of the decoder RNN dec at t is the hidden vector hdec . In gen- eral the encoder and decoder may be implemented by any recurrent neural network. In our experiments we use the Long Short-Term Memory architecture (LSTM; Hochreiter & Schmidhuber (1997)) for both, in the extended form with forget gates (Gers et al., 2000). We favour LSTM due to its proven track record for handling long-range depen- dencies in real sequential data (Graves, 2013; Sutskever et al., 2014). Throughout the paper, we use the notation b = W (a) to denote a linear weight matrix with bias from the vector a to the vector b.
At each time-step t, the encoder receives input from both the image x and from the previous decoder hidden vector hdec 1. The precise form of the encoder input depends on a t â read operation, which will be deï¬ned in the next section. The output henc of the encoder is used to parameterise a henc ) over the latent vector zt. In our distribution Q(Zt| t
DRAW: A Recurrent Neural Network For Image Generation | 1502.04623#8 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 8,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "Let RNN enc be the function enacted by the encoder net- work at a single time-step. The output of RNN enc at time t is the encoder hidden vector henc . Similarly the output of the decoder RNN dec at t is the hidden vector hdec . In gen- eral the encoder and decoder may be implemented by any recurrent neural network. In our experiments we use the Long Short-Term Memory architecture (LSTM; Hochreiter & Schmidhuber (1997)) for both, in the extended form with forget gates (Gers et al., 2000). We favour LSTM due to its proven track record for handling long-range depen- dencies in real sequential data (Graves, 2013; Sutskever et al., 2014). Throughout the paper, we use the notation b = W (a) to denote a linear weight matrix with bias from the vector a to the vector b.\nAt each time-step t, the encoder receives input from both the image x and from the previous decoder hidden vector hdec 1. The precise form of the encoder input depends on a t â read operation, which will be deï¬ned in the next section. The output henc of the encoder is used to parameterise a henc ) over the latent vector zt. In our distribution Q(Zt| t\nDRAW: A Recurrent Neural Network For Image Generation",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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] |
1502.04623 | 9 | DRAW: A Recurrent Neural Network For Image Generation
experiments the latent distribution is a diagonal Gaussian µt, Ït):
# (Zt|
# N
# µt = W (henc Ït = exp (W (henc
) t (1)
t )) (2)
Bernoulli distributions are more common than Gaussians for latent variables in auto-encoders (Dayan et al., 1995; Gregor et al., 2014); however a great advantage of Gaus- sian latents is that the gradient of a function of the sam- ples with respect to the distribution parameters can be eas- ily obtained using the so-called reparameterization trick (Kingma & Welling, 2014; Rezende et al., 2014). This makes it straightforward to back-propagate unbiased, low variance stochastic gradients of the loss function through the latent distribution. ) drawn from At each time-step a sample zt â¼ Q(Zt| the latent distribution is passed as input to the decoder. The output hdec of the decoder is added (via a write opera- tion, deï¬ned in the sequel) to a cumulative canvas matrix ct, which is ultimately used to reconstruct the image. The total number of time-steps T consumed by the network be- fore performing the reconstruction is a free parameter that must be speciï¬ed in advance. | 1502.04623#9 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 9,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "DRAW: A Recurrent Neural Network For Image Generation\nexperiments the latent distribution is a diagonal Gaussian µt, Ït):\n# (Zt|\n# N\n# µt = W (henc Ït = exp (W (henc\n) t (1)\nt )) (2)\nBernoulli distributions are more common than Gaussians for latent variables in auto-encoders (Dayan et al., 1995; Gregor et al., 2014); however a great advantage of Gaus- sian latents is that the gradient of a function of the sam- ples with respect to the distribution parameters can be eas- ily obtained using the so-called reparameterization trick (Kingma & Welling, 2014; Rezende et al., 2014). This makes it straightforward to back-propagate unbiased, low variance stochastic gradients of the loss function through the latent distribution. ) drawn from At each time-step a sample zt â¼ Q(Zt| the latent distribution is passed as input to the decoder. The output hdec of the decoder is added (via a write opera- tion, deï¬ned in the sequel) to a cumulative canvas matrix ct, which is ultimately used to reconstruct the image. The total number of time-steps T consumed by the network be- fore performing the reconstruction is a free parameter that must be speciï¬ed in advance.",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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1502.04623 | 10 | negative log probability of x under D:
x = (9) log D(x
# cT ) |
L z for a sequence of latent distributions ) is deï¬ned as the summed Kullback-Leibler diâ
The latent loss L* for a sequence of latent Q(Z,\hfâ*) is defined as the summed Kullback-Leibler vergence of some latent prior P(Z;) from T Le = S7 KL(Q(Z|he"*)||P(Z1)) t=1
Q(Z;|hf"*):
Le = S7 KL(Q(Z|he"*)||P(Z1)) (10) t=1 Note that this loss depends upon the latent samples z; drawn from Q(Z;|hf"°), which depend in turn on the input x. If the latent distribution is a diagonal Gaussian with ju;, o, as defined in Eqs | and 2, a simple choice for P(Z;) is a standard Gaussian with mean zero and standard deviation one, in which case Eq. 10 becomes
# T
T 1 Las (0 top - beet) âT/2 (lly t=1
The total loss sum of the reconstruction and latent losses: for the network is the expectation of the L
= x + z Q (12)
# L
# L
â¼ | 1502.04623#10 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 10,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "negative log probability of x under D:\nx = (9) log D(x\n# cT ) |\nL z for a sequence of latent distributions ) is deï¬ned as the summed Kullback-Leibler diâ\nThe latent loss L* for a sequence of latent Q(Z,\\hfâ*) is defined as the summed Kullback-Leibler vergence of some latent prior P(Z;) from T Le = S7 KL(Q(Z|he\"*)||P(Z1)) t=1\nQ(Z;|hf\"*):\nLe = S7 KL(Q(Z|he\"*)||P(Z1)) (10) t=1 Note that this loss depends upon the latent samples z; drawn from Q(Z;|hf\"°), which depend in turn on the input x. If the latent distribution is a diagonal Gaussian with ju;, o, as defined in Eqs | and 2, a simple choice for P(Z;) is a standard Gaussian with mean zero and standard deviation one, in which case Eq. 10 becomes\n# T\nT 1 Las (0 top - beet) âT/2 (lly t=1\nThe total loss sum of the reconstruction and latent losses: for the network is the expectation of the L\n= x + z Q (12)\n# L\n# L\nâ¼",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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1502.04623 | 11 | The total loss sum of the reconstruction and latent losses: for the network is the expectation of the L
= x + z Q (12)
# L
# L
â¼
For each image x presented to the network, c0, henc , hdec 0 are initialised to learned biases, and the DRAW net- work iteratively computes the following equations for t = 1 . . . , T :
1) (3)
Ï(ct Ëxt = x rt = read (xt, Ëxt, hdec 1) t â t = RNN enc(henc 1, [rt, hdec henc t â Q(Zt| zt â¼ t = RNN dec(hdec hdec ct = ct
â
â
(4)
1]) (5)
t â )
henc t (6)
(7)
1, zt) t 1 + write(hdec )
â
t (8)
â | 1502.04623#11 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 11,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "The total loss sum of the reconstruction and latent losses: for the network is the expectation of the L\n= x + z Q (12)\n# L\n# L\nâ¼\nFor each image x presented to the network, c0, henc , hdec 0 are initialised to learned biases, and the DRAW net- work iteratively computes the following equations for t = 1 . . . , T :\n1) (3)\nÏ(ct Ëxt = x rt = read (xt, Ëxt, hdec 1) t â t = RNN enc(henc 1, [rt, hdec henc t â Q(Zt| zt â¼ t = RNN dec(hdec hdec ct = ct\nâ\nâ\n(4)\n1]) (5)\nt â )\nhenc t (6)\n(7)\n1, zt) t 1 + write(hdec )\nâ\nt (8)\nâ",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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] |
1502.04623 | 12 | â
(4)
1]) (5)
t â )
henc t (6)
(7)
1, zt) t 1 + write(hdec )
â
t (8)
â
where Ëxt is the error image, [v, w] is the concatenation of vectors v and w into a single vector, and Ï denotes x) . Note the logistic sigmoid function: Ï(x) = that henc ), depends on both x , and hence Q(Zt| 1 of previous latent samples. We and the history z1:t will sometimes make this dependency explicit by writing 1), as shown in Fig. 2. henc can also be Q(Zt| â passed as input to the read operation; however we did not ï¬nd that this helped performance and therefore omitted it.
which we optimise using a single sample of z for each stochastic gradient descent step.
z can be interpreted as the number of nats required to L transmit the latent sample sequence z1:T to the decoder x is the number of from the prior, and (if x is discrete) nats required for the decoder to reconstruct x given z1:T . The total loss is therefore equivalent to the expected com- pression of the data by the decoder and prior.
# 2.3. Stochastic Data Generation | 1502.04623#12 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 12,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "â\n(4)\n1]) (5)\nt â )\nhenc t (6)\n(7)\n1, zt) t 1 + write(hdec )\nâ\nt (8)\nâ\nwhere Ëxt is the error image, [v, w] is the concatenation of vectors v and w into a single vector, and Ï denotes x) . Note the logistic sigmoid function: Ï(x) = that henc ), depends on both x , and hence Q(Zt| 1 of previous latent samples. We and the history z1:t will sometimes make this dependency explicit by writing 1), as shown in Fig. 2. henc can also be Q(Zt| â passed as input to the read operation; however we did not ï¬nd that this helped performance and therefore omitted it.\nwhich we optimise using a single sample of z for each stochastic gradient descent step.\nz can be interpreted as the number of nats required to L transmit the latent sample sequence z1:T to the decoder x is the number of from the prior, and (if x is discrete) nats required for the decoder to reconstruct x given z1:T . The total loss is therefore equivalent to the expected com- pression of the data by the decoder and prior.\n# 2.3. Stochastic Data Generation",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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] |
1502.04623 | 13 | # 2.3. Stochastic Data Generation
An image Ëx can be generated by a DRAW network by it- eratively picking latent samples Ëzt from the prior P , then running the decoder to update the canvas matrix Ëct. After T repetitions of this process the generated image is a sample from D(X
ËcT ): |
(13)
Ëzt â¼ P (Zt) t = RNN dec(Ëhdec Ëhdec Ëct = Ëct Ëx
(14)
# 1, Ëzt) t â 1 + write(Ëhdec )
t (15)
# â D(X
ËcT ) (16)
| Note that the encoder is not involved in image generation.
â¼
# 2.2. Loss Function
# 3. Read and Write Operations
The ï¬nal canvas matrix cT is used to parameterise a model cT ) of the input data. If the input is binary, the natural D(X choice for D is a Bernoulli distribution with means given x is deï¬ned as the by Ï(cT ). The reconstruction loss
The DRAW network described in the previous section is not complete until the read and write operations in Eqs. 4 and 8 have been deï¬ned. This section describes two ways to do so, one with selective attention and one without.
# L | 1502.04623#13 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 13,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "# 2.3. Stochastic Data Generation\nAn image Ëx can be generated by a DRAW network by it- eratively picking latent samples Ëzt from the prior P , then running the decoder to update the canvas matrix Ëct. After T repetitions of this process the generated image is a sample from D(X\nËcT ): |\n(13)\nËzt â¼ P (Zt) t = RNN dec(Ëhdec Ëhdec Ëct = Ëct Ëx\n(14)\n# 1, Ëzt) t â 1 + write(Ëhdec )\nt (15)\n# â D(X\nËcT ) (16)\n| Note that the encoder is not involved in image generation.\nâ¼\n# 2.2. Loss Function\n# 3. Read and Write Operations\nThe ï¬nal canvas matrix cT is used to parameterise a model cT ) of the input data. If the input is binary, the natural D(X choice for D is a Bernoulli distribution with means given x is deï¬ned as the by Ï(cT ). The reconstruction loss\nThe DRAW network described in the previous section is not complete until the read and write operations in Eqs. 4 and 8 have been deï¬ned. This section describes two ways to do so, one with selective attention and one without.\n# L",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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] |
1502.04623 | 14 | # L
DRAW: A Recurrent Neural Network For Image Generation
# 3.1. Reading and Writing Without Attention
In the simplest instantiation of DRAW the entire input im- age is passed to the encoder at every time-step, and the de- coder modiï¬es the entire canvas matrix at every time-step. In this case the read and write operations reduce to
# read (x, Ëxt, hdec â write(hdec
t (17)
1) = [x, Ëxt] ) = W (hdec
t t ) (18)
However this approach does not allow the encoder to fo- cus on only part of the input when creating the latent dis- tribution; nor does it allow the decoder to modify only a part of the canvas vector. In other words it does not pro- vide the network with an explicit selective attention mech- anism, which we believe to be crucial to large scale image generation. We refer to the above conï¬guration as âDRAW without attentionâ.
# 3.2. Selective Attention Model
> ) A gy | YY gx b' Ei | 1502.04623#14 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 14,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "# L\nDRAW: A Recurrent Neural Network For Image Generation\n# 3.1. Reading and Writing Without Attention\nIn the simplest instantiation of DRAW the entire input im- age is passed to the encoder at every time-step, and the de- coder modiï¬es the entire canvas matrix at every time-step. In this case the read and write operations reduce to\n# read (x, Ëxt, hdec â write(hdec\nt (17)\n1) = [x, Ëxt] ) = W (hdec\nt t ) (18)\nHowever this approach does not allow the encoder to fo- cus on only part of the input when creating the latent dis- tribution; nor does it allow the decoder to modify only a part of the canvas vector. In other words it does not pro- vide the network with an explicit selective attention mech- anism, which we believe to be crucial to large scale image generation. We refer to the above conï¬guration as âDRAW without attentionâ.\n# 3.2. Selective Attention Model\n> ) A gy | YY gx b' Ei",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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] |
1502.04623 | 15 | # 3.2. Selective Attention Model
> ) A gy | YY gx b' Ei
Figure 3. Left: A 3 à 3 grid of ï¬lters superimposed on an image. The stride (δ) and centre location (gX , gY ) are indicated. Right: Three N à N patches extracted from the image (N = 12). The green rectangles on the left indicate the boundary and precision (Ï) of the patches, while the patches themselves are shown to the right. The top patch has a small δ and high Ï, giving a zoomed-in but blurry view of the centre of the digit; the middle patch has large δ and low Ï, effectively downsampling the whole image; and the bottom patch has high δ and Ï. | 1502.04623#15 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 15,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "# 3.2. Selective Attention Model\n> ) A gy | YY gx b' Ei\nFigure 3. Left: A 3 à 3 grid of ï¬lters superimposed on an image. The stride (δ) and centre location (gX , gY ) are indicated. Right: Three N à N patches extracted from the image (N = 12). The green rectangles on the left indicate the boundary and precision (Ï) of the patches, while the patches themselves are shown to the right. The top patch has a small δ and high Ï, giving a zoomed-in but blurry view of the centre of the digit; the middle patch has large δ and low Ï, effectively downsampling the whole image; and the bottom patch has high δ and Ï.",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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1502.04623 | 16 | To endow the network with selective attention without sac- riï¬cing the beneï¬ts of gradient descent training, we take in- spiration from the differentiable attention mechanisms re- cently used in handwriting synthesis (Graves, 2013) and Neural Turing Machines (Graves et al., 2014). Unlike the aforementioned works, we consider an explicitly two- dimensional form of attention, where an array of 2D Gaus- sian ï¬lters is applied to the image, yielding an image âpatchâ of smoothly varying location and zoom. This con- ï¬guration, which we refer to simply as âDRAWâ, some- what resembles the afï¬ne transformations used in computer graphics-based autoencoders (Tieleman, 2014).
# via a linear transformation of the decoder output hdec:
(ËgX , ËgY , log Ï2, log Ëδ, log γ) = W (hdec) (21)
A + 1 2 B + 1 2 max(A, B) 1 N
gX = (ËgX + 1) (22)
gY = (ËgY + 1) (23)
1 Ëδ δ = â (24) | 1502.04623#16 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 16,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "To endow the network with selective attention without sac- riï¬cing the beneï¬ts of gradient descent training, we take in- spiration from the differentiable attention mechanisms re- cently used in handwriting synthesis (Graves, 2013) and Neural Turing Machines (Graves et al., 2014). Unlike the aforementioned works, we consider an explicitly two- dimensional form of attention, where an array of 2D Gaus- sian ï¬lters is applied to the image, yielding an image âpatchâ of smoothly varying location and zoom. This con- ï¬guration, which we refer to simply as âDRAWâ, some- what resembles the afï¬ne transformations used in computer graphics-based autoencoders (Tieleman, 2014).\n# via a linear transformation of the decoder output hdec:\n(ËgX , ËgY , log Ï2, log Ëδ, log γ) = W (hdec) (21)\nA + 1 2 B + 1 2 max(A, B) 1 N\ngX = (ËgX + 1) (22)\ngY = (ËgY + 1) (23)\n1 Ëδ δ = â (24)",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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] |
1502.04623 | 17 | gX = (ËgX + 1) (22)
gY = (ËgY + 1) (23)
1 Ëδ δ = â (24)
N grid of Gaussian ï¬lters is As illustrated in Fig. 3, the N à positioned on the image by specifying the co-ordinates of the grid centre and the stride distance between adjacent ï¬l- ters. The stride controls the âzoomâ of the patch; that is, the larger the stride, the larger an area of the original image will be visible in the attention patch, but the lower the effective resolution of the patch will be. The grid centre (gX , gY ) and stride δ (both of which are real-valued) determine the mean location µi Y of the ï¬lter at row i, column j in the patch as follows:
â
where the variance, stride and intensity are emitted in the log-scale to ensure positivity. The scaling of gX , gY and δ is chosen to ensure that the initial patch (with a randomly initialised network) roughly covers the whole input image.
Given the attention parameters emitted by the decoder, the horizontal and vertical ï¬lterbank matrices FX and FY (di- B respectively) are deï¬ned as mensions N follows: | 1502.04623#17 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 17,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "gX = (ËgX + 1) (22)\ngY = (ËgY + 1) (23)\n1 Ëδ δ = â (24)\nN grid of Gaussian ï¬lters is As illustrated in Fig. 3, the N à positioned on the image by specifying the co-ordinates of the grid centre and the stride distance between adjacent ï¬l- ters. The stride controls the âzoomâ of the patch; that is, the larger the stride, the larger an area of the original image will be visible in the attention patch, but the lower the effective resolution of the patch will be. The grid centre (gX , gY ) and stride δ (both of which are real-valued) determine the mean location µi Y of the ï¬lter at row i, column j in the patch as follows:\nâ\nwhere the variance, stride and intensity are emitted in the log-scale to ensure positivity. The scaling of gX , gY and δ is chosen to ensure that the initial patch (with a randomly initialised network) roughly covers the whole input image.\nGiven the attention parameters emitted by the decoder, the horizontal and vertical ï¬lterbank matrices FX and FY (di- B respectively) are deï¬ned as mensions N follows:",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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] |
1502.04623 | 18 | µi X = gX + (i µj Y = gY + (j
N/2 0.5) δ (19)
â
â
N/2 0.5) δ (20)
â
â
Two more parameters are required to fully specify the at- tention model: the isotropic variance Ï2 of the Gaussian ï¬lters, and a scalar intensity γ that multiplies the ï¬lter re- sponse. Given an A B input image x, all ï¬ve attention parameters are dynamically determined at each time step
X )2 µi â 2Ï2 µj Y )2 â 2Ï2
FX [i, a] = 1 ZX exp â (a (25)
FY [j, b] = 1 ZY exp â (b (26)
where (i, 7) is a point in the attention patch, (a, b) is a point in the input image, and Z,, Z, are normalisation constants that ensure that )>, Fx [i,a] = 1 and >, Fy[j, 6] = 1.
DRAW: A Recurrent Neural Network For Image Generation | 1502.04623#18 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 18,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "µi X = gX + (i µj Y = gY + (j\nN/2 0.5) δ (19)\nâ\nâ\nN/2 0.5) δ (20)\nâ\nâ\nTwo more parameters are required to fully specify the at- tention model: the isotropic variance Ï2 of the Gaussian ï¬lters, and a scalar intensity γ that multiplies the ï¬lter re- sponse. Given an A B input image x, all ï¬ve attention parameters are dynamically determined at each time step\nX )2 µi â 2Ï2 µj Y )2 â 2Ï2\nFX [i, a] = 1 ZX exp â (a (25)\nFY [j, b] = 1 ZY exp â (b (26)\nwhere (i, 7) is a point in the attention patch, (a, b) is a point in the input image, and Z,, Z, are normalisation constants that ensure that )>, Fx [i,a] = 1 and >, Fy[j, 6] = 1.\nDRAW: A Recurrent Neural Network For Image Generation",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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1502.04623 | 19 | Figure 4. Zooming. Top Left: The original 100 à 75 image. Top Middle: A 12 à 12 patch extracted with 144 2D Gaussian ï¬lters. Top Right: The reconstructed image when applying transposed ï¬lters on the patch. Bottom: Only two 2D Gaussian ï¬lters are displayed. The ï¬rst one is used to produce the top-left patch fea- ture. The last ï¬lter is used to produce the bottom-right patch fea- ture. By using different ï¬lter weights, the attention can be moved to a different location.
generated by the network are always novel (not simply copies of training examples), and are virtually indistin- guishable from real data for MNIST and SVHN; the gener- ated CIFAR images are somewhat blurry, but still contain recognisable structure from natural scenes. The binarized MNIST results substantially improve on the state of the art. As a preliminary exercise, we also evaluate the 2D atten- tion module of the DRAW network on cluttered MNIST classiï¬cation. | 1502.04623#19 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 19,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "Figure 4. Zooming. Top Left: The original 100 à 75 image. Top Middle: A 12 à 12 patch extracted with 144 2D Gaussian ï¬lters. Top Right: The reconstructed image when applying transposed ï¬lters on the patch. Bottom: Only two 2D Gaussian ï¬lters are displayed. The ï¬rst one is used to produce the top-left patch fea- ture. The last ï¬lter is used to produce the bottom-right patch fea- ture. By using different ï¬lter weights, the attention can be moved to a different location.\ngenerated by the network are always novel (not simply copies of training examples), and are virtually indistin- guishable from real data for MNIST and SVHN; the gener- ated CIFAR images are somewhat blurry, but still contain recognisable structure from natural scenes. The binarized MNIST results substantially improve on the state of the art. As a preliminary exercise, we also evaluate the 2D atten- tion module of the DRAW network on cluttered MNIST classiï¬cation.",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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] |
1502.04623 | 20 | For all experiments, the model D(X cT ) of the input data | was a Bernoulli distribution with means given by Ï(cT ). For the MNIST experiments, the reconstruction loss from Eq 9 was the usual binary cross-entropy term. For the SVHN and CIFAR-10 experiments, the red, green and blue pixel intensities were represented as numbers between 0 and 1, which were then interpreted as independent colour emission probabilities. The reconstruction loss was there- fore the cross-entropy between the pixel intensities and the model probabilities. Although this approach worked well in practice, it means that the training loss did not corre- spond to the true compression cost of RGB images.
# 3.3. Reading and Writing With Attention
Given FX , FY and intensity γ determined by hdec 1, along â with an input image x and error image Ëxt, the read opera- tion returns the concatenation of two N N patches from the image and error image:
the experiments are Network hyper-parameters for all presented in Table 3. The Adam optimisation algo- rithm (Kingma & Ba, 2014) was used throughout. Ex- amples of generation sequences for MNIST and SVHN are provided in the accompanying video (https://www. youtube.com/watch?v=Zt-7MI9eKEo). | 1502.04623#20 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 20,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "For all experiments, the model D(X cT ) of the input data | was a Bernoulli distribution with means given by Ï(cT ). For the MNIST experiments, the reconstruction loss from Eq 9 was the usual binary cross-entropy term. For the SVHN and CIFAR-10 experiments, the red, green and blue pixel intensities were represented as numbers between 0 and 1, which were then interpreted as independent colour emission probabilities. The reconstruction loss was there- fore the cross-entropy between the pixel intensities and the model probabilities. Although this approach worked well in practice, it means that the training loss did not corre- spond to the true compression cost of RGB images.\n# 3.3. Reading and Writing With Attention\nGiven FX , FY and intensity γ determined by hdec 1, along â with an input image x and error image Ëxt, the read opera- tion returns the concatenation of two N N patches from the image and error image:\nthe experiments are Network hyper-parameters for all presented in Table 3. The Adam optimisation algo- rithm (Kingma & Ba, 2014) was used throughout. Ex- amples of generation sequences for MNIST and SVHN are provided in the accompanying video (https://www. youtube.com/watch?v=Zt-7MI9eKEo).",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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1502.04623 | 21 | 1) = γ[FY xF T X , FY ËxF T X ] (27)
# read (x, Ëxt, hdec t â
Note that the same ï¬lterbanks are used for both the image and error image. For the write operation, a distinct set of attention parameters Ëγ, ËFX and ËFY are extracted from hdec , the order of transposition is reversed, and the intensity is inverted:
# wt = W (hdec 1 Ëγ
) t (28)
Y wt ËFX ËF T write(hdec ) = t (29)
# 4.1. Cluttered MNIST Classiï¬cation
To test the classiï¬cation efï¬cacy of the DRAW attention mechanism (as opposed to its ability to aid in image gener- ation), we evaluate its performance on the 100 100 clut- tered translated MNIST task (Mnih et al., 2014). Each im- age in cluttered MNIST contains many digit-like fragments of visual clutter that the network must distinguish from the true digit to be classiï¬ed. As illustrated in Fig. 5, having an iterative attention model allows the network to progres- sively zoom in on the relevant region of the image, and ignore the clutter outside it. | 1502.04623#21 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 21,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "1) = γ[FY xF T X , FY ËxF T X ] (27)\n# read (x, Ëxt, hdec t â\nNote that the same ï¬lterbanks are used for both the image and error image. For the write operation, a distinct set of attention parameters Ëγ, ËFX and ËFY are extracted from hdec , the order of transposition is reversed, and the intensity is inverted:\n# wt = W (hdec 1 Ëγ\n) t (28)\nY wt ËFX ËF T write(hdec ) = t (29)\n# 4.1. Cluttered MNIST Classiï¬cation\nTo test the classiï¬cation efï¬cacy of the DRAW attention mechanism (as opposed to its ability to aid in image gener- ation), we evaluate its performance on the 100 100 clut- tered translated MNIST task (Mnih et al., 2014). Each im- age in cluttered MNIST contains many digit-like fragments of visual clutter that the network must distinguish from the true digit to be classiï¬ed. As illustrated in Fig. 5, having an iterative attention model allows the network to progres- sively zoom in on the relevant region of the image, and ignore the clutter outside it.",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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] |
1502.02072 | 50 | Dataset pcba-aid602310 pcba-aid602313 pcba-aid602332 pcba-aid624170 pcba-aid624171 pcba-aid624173 pcba-aid624202 pcba-aid624246 pcba-aid624287 pcba-aid624288 pcba-aid624291 pcba-aid624296* pcba-aid624297* pcba-aid624417 pcba-aid651635 pcba-aid651644 pcba-aid651768 pcba-aid651965 pcba-aid652025 pcba-aid652104 pcba-aid652105 pcba-aid652106 pcba-aid686970 pcba-aid686978* pcba-aid686979* pcba-aid720504 pcba-aid720532* pcba-aid720542 Actives Inactives Target Class 310 762 70 837 1239 488 3968 101 423 1356 222 9841 6214 6388 3784 748 1677 6422 238 7126 4072 496 5949 62 746 48 816 10 170 945 733 402 026 383 076 415 773 404 440 402 621 406 224 372 045 367 273 334 388 336 077 345 619 333 378 336 050 398 731 387 779 361 115 362 320 331 953 364 365 396 566 | 1502.02072#50 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 50,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "Dataset pcba-aid602310 pcba-aid602313 pcba-aid602332 pcba-aid624170 pcba-aid624171 pcba-aid624173 pcba-aid624202 pcba-aid624246 pcba-aid624287 pcba-aid624288 pcba-aid624291 pcba-aid624296* pcba-aid624297* pcba-aid624417 pcba-aid651635 pcba-aid651644 pcba-aid651768 pcba-aid651965 pcba-aid652025 pcba-aid652104 pcba-aid652105 pcba-aid652106 pcba-aid686970 pcba-aid686978* pcba-aid686979* pcba-aid720504 pcba-aid720532* pcba-aid720542 Actives Inactives Target Class 310 762 70 837 1239 488 3968 101 423 1356 222 9841 6214 6388 3784 748 1677 6422 238 7126 4072 496 5949 62 746 48 816 10 170 945 733 402 026 383 076 415 773 404 440 402 621 406 224 372 045 367 273 334 388 336 077 345 619 333 378 336 050 398 731 387 779 361 115 362 320 331 953 364 365 396 566",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 51 | 773 404 440 402 621 406 224 372 045 367 273 334 388 336 077 345 619 333 378 336 050 398 731 387 779 361 115 362 320 331 953 364 365 396 566 324 774 368 281 358 501 354 086 368 048 353 881 14 532 363 349 path- path- path- Target Vif-A3G Vif-A3F GRP78 GLS Nrf2 PYK BRCA1 ERG Gsgsp Gsgsp a7 DNA re-replication DNA re-replication GLP-1 ATXN Vpr WRN ClpP IL-2 TDP-43 PI5P4K alpha-synuclein HT-1080-NT DT40-hTDP1 DT40-hTDP1 Plk1 PBD Marburg virus AMA1-RON2 | 1502.02072#51 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 51,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "773 404 440 402 621 406 224 372 045 367 273 334 388 336 077 345 619 333 378 336 050 398 731 387 779 361 115 362 320 331 953 364 365 396 566 324 774 368 281 358 501 354 086 368 048 353 881 14 532 363 349 path- path- path- Target Vif-A3G Vif-A3F GRP78 GLS Nrf2 PYK BRCA1 ERG Gsgsp Gsgsp a7 DNA re-replication DNA re-replication GLP-1 ATXN Vpr WRN ClpP IL-2 TDP-43 PI5P4K alpha-synuclein HT-1080-NT DT40-hTDP1 DT40-hTDP1 Plk1 PBD Marburg virus AMA1-RON2",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 52 | protein-protein interaction protein-protein interaction promoter other enzyme transcription fac- tor other enzyme promoter miscellaneous signalling way signalling way promoter miscellaneous miscellaneous GPCR promoter miscellaneous other enzyme protease signalling way miscellaneous other enzyme miscellaneous viability viability viability protein kinase miscellaneous protein-protein interaction ion channel ion channel miscellaneous miscellaneous other enzyme other enzyme other enzyme other enzyme protease GPCR GPCR protein kinase transcription fac- tor protein kinase other enzyme other receptor
1265 3260 1913 1508 268 661 516 290 902 306 30 30 30
342 387 338 810 304 815 324 844 364 332 363 939 364 084 364 310 388 656 405 368 14 999 15 000 14 999
pcba-aid720551* pcba-aid720553* pcba-aid720579* pcba-aid720580* pcba-aid720707 pcba-aid720708 pcba-aid720709 pcba-aid720711 pcba-aid743255 pcba-aid743266 muv-aid466 muv-aid548 muv-aid600
KCHN2 3.1 KCHN2 3.1 orthopoxvirus orthopoxvirus EPAC1 EPAC2 EPAC1 EPAC2 USP1/UAF1 PTHR1 S1P1 receptor PKA SF1 | 1502.02072#52 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 52,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "protein-protein interaction protein-protein interaction promoter other enzyme transcription fac- tor other enzyme promoter miscellaneous signalling way signalling way promoter miscellaneous miscellaneous GPCR promoter miscellaneous other enzyme protease signalling way miscellaneous other enzyme miscellaneous viability viability viability protein kinase miscellaneous protein-protein interaction ion channel ion channel miscellaneous miscellaneous other enzyme other enzyme other enzyme other enzyme protease GPCR GPCR protein kinase transcription fac- tor protein kinase other enzyme other receptor\n1265 3260 1913 1508 268 661 516 290 902 306 30 30 30\n342 387 338 810 304 815 324 844 364 332 363 939 364 084 364 310 388 656 405 368 14 999 15 000 14 999\npcba-aid720551* pcba-aid720553* pcba-aid720579* pcba-aid720580* pcba-aid720707 pcba-aid720708 pcba-aid720709 pcba-aid720711 pcba-aid743255 pcba-aid743266 muv-aid466 muv-aid548 muv-aid600\nKCHN2 3.1 KCHN2 3.1 orthopoxvirus orthopoxvirus EPAC1 EPAC2 EPAC1 EPAC2 USP1/UAF1 PTHR1 S1P1 receptor PKA SF1",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 54 | Dataset muv-aid692 muv-aid712* muv-aid713* muv-aid733 muv-aid737* muv-aid810* muv-aid832 muv-aid846 muv-aid852 muv-aid858 muv-aid859 tox-NR-AhR tox-NR-AR-LBD* tox-NR-AR* tox-NR-Aromatase tox-NR-ER-LBD* tox-NR-ER* tox-NR-PPAR-gamma* tox-SR-ARE tox-SR-ATAD5 tox-SR-HSE tox-SR-MMP tox-SR-p53 dude-aa2ar dude-abl1 dude-ace dude-aces dude-ada dude-ada17 dude-adrb1 dude-adrb2 dude-akt1 dude-akt2 Actives Inactives Target Class 30 30 30 30 30 30 30 30 30 30 30 768 237 309 300 350 793 186 942 264 372 919 423 482 182 282 453 93 532 247 231 293 15 000 14 997 15 000 15 000 14 999 14 999 15 000 15 000 15 000 14 999 15 000 5780 6520 6955 5521 6604 5399 6263 4889 6807 | 1502.02072#54 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 54,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "Dataset muv-aid692 muv-aid712* muv-aid713* muv-aid733 muv-aid737* muv-aid810* muv-aid832 muv-aid846 muv-aid852 muv-aid858 muv-aid859 tox-NR-AhR tox-NR-AR-LBD* tox-NR-AR* tox-NR-Aromatase tox-NR-ER-LBD* tox-NR-ER* tox-NR-PPAR-gamma* tox-SR-ARE tox-SR-ATAD5 tox-SR-HSE tox-SR-MMP tox-SR-p53 dude-aa2ar dude-abl1 dude-ace dude-aces dude-ada dude-ada17 dude-adrb1 dude-adrb2 dude-akt1 dude-akt2 Actives Inactives Target Class 30 30 30 30 30 30 30 30 30 30 30 768 237 309 300 350 793 186 942 264 372 919 423 482 182 282 453 93 532 247 231 293 15 000 14 997 15 000 15 000 14 999 14 999 15 000 15 000 15 000 14 999 15 000 5780 6520 6955 5521 6604 5399 6263 4889 6807",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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1502.02072 | 55 | 15 000 14 997 15 000 15 000 14 999 14 999 15 000 15 000 15 000 14 999 15 000 5780 6520 6955 5521 6604 5399 6263 4889 6807 6094 4891 6351 31 546 10 749 16 899 26 240 5450 35 900 15 848 14 997 16 441 transcription fac- tor miscellaneous protein-protein interaction protein-protein interaction protein-protein interaction protein kinase protease protease protease GPCR GPCR transcription fac- tor transcription fac- tor transcription fac- tor other enzyme transcription fac- tor transcription fac- tor transcription fac- tor miscellaneous promoter miscellaneous miscellaneous miscellaneous GPCR protein kinase protease other enzyme other enzyme protease GPCR GPCR protein kinase 117 6899 protein kinase Target SF1 HSP90 ER-a-coact. bind. ER-b-coact. bind. ER-a-coact. bind. FAK Cathepsin G FXIa FXIIa D1 receptor M1 receptor Aryl hydrocarbon receptor Androgen receptor Androgen receptor Aromatase Estrogen receptor alpha Estrogen receptor alpha PPARg kinase kinase | 1502.02072#55 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 55,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "15 000 14 997 15 000 15 000 14 999 14 999 15 000 15 000 15 000 14 999 15 000 5780 6520 6955 5521 6604 5399 6263 4889 6807 6094 4891 6351 31 546 10 749 16 899 26 240 5450 35 900 15 848 14 997 16 441 transcription fac- tor miscellaneous protein-protein interaction protein-protein interaction protein-protein interaction protein kinase protease protease protease GPCR GPCR transcription fac- tor transcription fac- tor transcription fac- tor other enzyme transcription fac- tor transcription fac- tor transcription fac- tor miscellaneous promoter miscellaneous miscellaneous miscellaneous GPCR protein kinase protease other enzyme other enzyme protease GPCR GPCR protein kinase 117 6899 protein kinase Target SF1 HSP90 ER-a-coact. bind. ER-b-coact. bind. ER-a-coact. bind. FAK Cathepsin G FXIa FXIIa D1 receptor M1 receptor Aryl hydrocarbon receptor Androgen receptor Androgen receptor Aromatase Estrogen receptor alpha Estrogen receptor alpha PPARg kinase kinase",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 56 | ARE ATAD5 HSE mitochondrial membrane potential p53 signalling Adenosine A2a receptor Tyrosine-protein kinase ABL Angiotensin-converting enzyme Acetylcholinesterase Adenosine deaminase ADAM17 Beta-1 adrenergic receptor Beta-2 adrenergic receptor Serine/threonine-protein AKT Serine/threonine-protein AKT2 Aldose reductase Beta-lactamase Androgen Receptor
159 48 269
8999 2850 14 350
# dude-aldr dude-ampc dude-andr*
# other enzyme other enzyme transcription fac- tor other enzyme protease
122 283
6900 18 097
# dude-aofb dude-bace1
# Monoamine oxidase B Beta-secretase 1
Massively Multitask Networks for Drug Discovery | 1502.02072#56 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 56,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "ARE ATAD5 HSE mitochondrial membrane potential p53 signalling Adenosine A2a receptor Tyrosine-protein kinase ABL Angiotensin-converting enzyme Acetylcholinesterase Adenosine deaminase ADAM17 Beta-1 adrenergic receptor Beta-2 adrenergic receptor Serine/threonine-protein AKT Serine/threonine-protein AKT2 Aldose reductase Beta-lactamase Androgen Receptor\n159 48 269\n8999 2850 14 350\n# dude-aldr dude-ampc dude-andr*\n# other enzyme other enzyme transcription fac- tor other enzyme protease\n122 283\n6900 18 097\n# dude-aofb dude-bace1\n# Monoamine oxidase B Beta-secretase 1\nMassively Multitask Networks for Drug Discovery",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 57 | Dataset dude-braf dude-cah2 dude-casp3 dude-cdk2 dude-comt dude-cp2c9 dude-cp3a4 dude-csf1r dude-cxcr4 dude-def dude-dhi1 dude-dpp4 dude-drd3 dude-dyr dude-egfr dude-esr1* dude-esr2 dude-fa10 dude-fa7 dude-fabp4 dude-fak1* dude-fgfr1 dude-fkb1a dude-fnta dude-fpps dude-gcr dude-glcm* dude-gria2 dude-grik1 dude-hdac2 dude-hdac8 dude-hivint dude-hivpr Actives Inactives Target Class 152 9950 protein kinase 492 199 474 41 120 170 166 31 168 10 700 27 850 3850 7449 11 800 12 149 other enzyme protease protein kinase other enzyme other enzyme other enzyme other receptor 40 102 330 3406 5700 19 350 GPCR other enzyme other enzyme 533 480 231 542 40 943 34 037 17 192 35 047 protease GPCR other enzyme other receptor 383 | 1502.02072#57 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 57,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "Dataset dude-braf dude-cah2 dude-casp3 dude-cdk2 dude-comt dude-cp2c9 dude-cp3a4 dude-csf1r dude-cxcr4 dude-def dude-dhi1 dude-dpp4 dude-drd3 dude-dyr dude-egfr dude-esr1* dude-esr2 dude-fa10 dude-fa7 dude-fabp4 dude-fak1* dude-fgfr1 dude-fkb1a dude-fnta dude-fpps dude-gcr dude-glcm* dude-gria2 dude-grik1 dude-hdac2 dude-hdac8 dude-hivint dude-hivpr Actives Inactives Target Class 152 9950 protein kinase 492 199 474 41 120 170 166 31 168 10 700 27 850 3850 7449 11 800 12 149 other enzyme protease protein kinase other enzyme other enzyme other enzyme other receptor 40 102 330 3406 5700 19 350 GPCR other enzyme other enzyme 533 480 231 542 40 943 34 037 17 192 35 047 protease GPCR other enzyme other receptor 383",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 58 | 19 350 GPCR other enzyme other enzyme 533 480 231 542 40 943 34 037 17 192 35 047 protease GPCR other enzyme other receptor 383 367 537 114 47 20 675 20 190 28 315 6250 2750 transcription fac- tor transcription fac- tor protease protease miscellaneous 100 139 111 592 5350 8697 5800 51 481 protein kinase other receptor other enzyme other enzyme 85 258 54 158 101 8829 14 999 3800 11 842 6549 other enzyme transcription fac- tor other enzyme ion channel ion channel 185 170 100 10 299 10 449 6650 other enzyme other enzyme other enzyme 536 35 746 protease Target Serine/threonine-protein kinase B- raf Carbonic anhydrase II Caspase-3 Cyclin-dependent kinase 2 Catechol O-methyltransferase Cytochrome P450 2C9 Cytochrome P450 3A4 Macrophage colony stimulating factor receptor C-X-C chemokine receptor type 4 Peptide deformylase 11-beta-hydroxysteroid dehydro- genase 1 Dipeptidyl peptidase IV Dopamine D3 receptor Dihydrofolate reductase Epidermal growth factor receptor erbB1 Estrogen receptor alpha Estrogen receptor beta | 1502.02072#58 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 58,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "19 350 GPCR other enzyme other enzyme 533 480 231 542 40 943 34 037 17 192 35 047 protease GPCR other enzyme other receptor 383 367 537 114 47 20 675 20 190 28 315 6250 2750 transcription fac- tor transcription fac- tor protease protease miscellaneous 100 139 111 592 5350 8697 5800 51 481 protein kinase other receptor other enzyme other enzyme 85 258 54 158 101 8829 14 999 3800 11 842 6549 other enzyme transcription fac- tor other enzyme ion channel ion channel 185 170 100 10 299 10 449 6650 other enzyme other enzyme other enzyme 536 35 746 protease Target Serine/threonine-protein kinase B- raf Carbonic anhydrase II Caspase-3 Cyclin-dependent kinase 2 Catechol O-methyltransferase Cytochrome P450 2C9 Cytochrome P450 3A4 Macrophage colony stimulating factor receptor C-X-C chemokine receptor type 4 Peptide deformylase 11-beta-hydroxysteroid dehydro- genase 1 Dipeptidyl peptidase IV Dopamine D3 receptor Dihydrofolate reductase Epidermal growth factor receptor erbB1 Estrogen receptor alpha Estrogen receptor beta",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 59 | IV Dopamine D3 receptor Dihydrofolate reductase Epidermal growth factor receptor erbB1 Estrogen receptor alpha Estrogen receptor beta Coagulation factor X Coagulation factor VII Fatty binding acid adipocyte FAK Fibroblast growth factor receptor 1 FK506-binding protein 1A Protein ferase/geranylgeranyltransferase type I alpha subunit Farnesyl diphosphate synthase Glucocorticoid receptor protein farnesyltrans- receptor ionotropic | 1502.02072#59 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 59,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "IV Dopamine D3 receptor Dihydrofolate reductase Epidermal growth factor receptor erbB1 Estrogen receptor alpha Estrogen receptor beta Coagulation factor X Coagulation factor VII Fatty binding acid adipocyte FAK Fibroblast growth factor receptor 1 FK506-binding protein 1A Protein ferase/geranylgeranyltransferase type I alpha subunit Farnesyl diphosphate synthase Glucocorticoid receptor protein farnesyltrans- receptor ionotropic",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 60 | glucocerebrosidase Glutamate receptor ionotropic Glutamate kainate 1 Histone deacetylase 2 Histone deacetylase 8 Human immunodeï¬ciency virus type 1 integrase Human immunodeï¬ciency virus type 1 protease Human immunodeï¬ciency virus type 1 reverse transcriptase HMG-CoA reductase HSP90 Hexokinase type IV
338
18 891
# dude-hivrt
# other enzyme
# dude-hmdh dude-hs90a* dude-hxk4
170 88 92
8748 4849 4700
# other enzyme miscellaneous other enzyme
Massively Multitask Networks for Drug Discovery | 1502.02072#60 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 60,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "glucocerebrosidase Glutamate receptor ionotropic Glutamate kainate 1 Histone deacetylase 2 Histone deacetylase 8 Human immunodeï¬ciency virus type 1 integrase Human immunodeï¬ciency virus type 1 protease Human immunodeï¬ciency virus type 1 reverse transcriptase HMG-CoA reductase HSP90 Hexokinase type IV\n338\n18 891\n# dude-hivrt\n# other enzyme\n# dude-hmdh dude-hs90a* dude-hxk4\n170 88 92\n8748 4849 4700\n# other enzyme miscellaneous other enzyme\nMassively Multitask Networks for Drug Discovery",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 61 | Dataset dude-igf1r dude-inha dude-ital dude-jak2 dude-kif11 dude-kit dude-kith dude-kpcb dude-lck dude-lkha4 dude-mapk2 dude-mcr dude-met dude-mk01 dude-mk10 dude-mk14 dude-mmp13 dude-mp2k1 dude-nos1 dude-nram dude-pa2ga dude-parp1 dude-pde5a dude-pgh1 dude-pgh2 dude-plk1 dude-pnph dude-ppara dude-ppard dude-pparg* dude-prgr Actives Inactives Target Class 148 9298 other receptor 43 2300 other enzyme 138 8498 miscellaneous 107 116 166 57 135 420 171 101 6499 6849 10 449 2849 8700 27 397 9450 6150 protein kinase miscellaneous other receptor other enzyme protein kinase protein kinase protease protein kinase 94 166 79 104 578 572 121 5150 11 247 4549 6600 35 848 37 195 8149 transcription fac- tor other receptor protein kinase protein kinase protein kinase protease protein kinase 100 98 99 508 398 195 435 107 8048 | 1502.02072#61 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 61,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "Dataset dude-igf1r dude-inha dude-ital dude-jak2 dude-kif11 dude-kit dude-kith dude-kpcb dude-lck dude-lkha4 dude-mapk2 dude-mcr dude-met dude-mk01 dude-mk10 dude-mk14 dude-mmp13 dude-mp2k1 dude-nos1 dude-nram dude-pa2ga dude-parp1 dude-pde5a dude-pgh1 dude-pgh2 dude-plk1 dude-pnph dude-ppara dude-ppard dude-pparg* dude-prgr Actives Inactives Target Class 148 9298 other receptor 43 2300 other enzyme 138 8498 miscellaneous 107 116 166 57 135 420 171 101 6499 6849 10 449 2849 8700 27 397 9450 6150 protein kinase miscellaneous other receptor other enzyme protein kinase protein kinase protease protein kinase 94 166 79 104 578 572 121 5150 11 247 4549 6600 35 848 37 195 8149 transcription fac- tor other receptor protein kinase protein kinase protein kinase protease protein kinase 100 98 99 508 398 195 435 107 8048",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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1502.02072 | 62 | 35 848 37 195 8149 transcription fac- tor other receptor protein kinase protein kinase protein kinase protease protein kinase 100 98 99 508 398 195 435 107 8048 6199 5150 30 049 27 547 10 800 23 149 6800 other enzyme other enzyme other enzyme other enzyme other enzyme other enzyme other enzyme protein kinase 103 373 240 484 293 6950 19 397 12 247 25 296 15 648 Target Insulin-like growth factor I recep- tor Enoyl-[acyl-carrier-protein] reductase Leukocyte adhesion glycoprotein LFA-1 alpha Tyrosine-protein kinase JAK2 Kinesin-like protein 1 Stem cell growth factor receptor Thymidine kinase Protein kinase C beta Tyrosine-protein kinase LCK Leukotriene A4 hydrolase MAP kinase-activated protein ki- nase 2 Mineralocorticoid receptor Hepatocyte growth factor receptor MAP kinase ERK2 c-Jun N-terminal kinase 3 MAP kinase p38 alpha Matrix metalloproteinase 13 Dual speciï¬city mitogen-activated protein kinase kinase 1 Nitric-oxide synthase Neuraminidase Phospholipase A2 group IIA Poly [ADP-ribose] polymerase-1 | 1502.02072#62 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 62,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "35 848 37 195 8149 transcription fac- tor other receptor protein kinase protein kinase protein kinase protease protein kinase 100 98 99 508 398 195 435 107 8048 6199 5150 30 049 27 547 10 800 23 149 6800 other enzyme other enzyme other enzyme other enzyme other enzyme other enzyme other enzyme protein kinase 103 373 240 484 293 6950 19 397 12 247 25 296 15 648 Target Insulin-like growth factor I recep- tor Enoyl-[acyl-carrier-protein] reductase Leukocyte adhesion glycoprotein LFA-1 alpha Tyrosine-protein kinase JAK2 Kinesin-like protein 1 Stem cell growth factor receptor Thymidine kinase Protein kinase C beta Tyrosine-protein kinase LCK Leukotriene A4 hydrolase MAP kinase-activated protein ki- nase 2 Mineralocorticoid receptor Hepatocyte growth factor receptor MAP kinase ERK2 c-Jun N-terminal kinase 3 MAP kinase p38 alpha Matrix metalloproteinase 13 Dual speciï¬city mitogen-activated protein kinase kinase 1 Nitric-oxide synthase Neuraminidase Phospholipase A2 group IIA Poly [ADP-ribose] polymerase-1",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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1502.02072 | 64 | other enzyme transcription fac- tor transcription fac- tor transcription fac- tor transcription fac- tor other enzyme other enzyme other enzyme other enzyme protease protein kinase transcription fac- tor other enzyme protein kinase
# dude-ptn1 dude-pur2 dude-pygm dude-pyrd dude-reni dude-rock1 dude-rxra
130 50 77 111 104 100 131
7250 2698 3948 6450 6958 6299 6948
Protein-tyrosine phosphatase 1B GAR transformylase Muscle glycogen phosphorylase Dihydroorotate dehydrogenase Renin Rho-associated protein kinase 1 Retinoid X receptor alpha
# dude-sahh dude-src
63 524
3450 34 491
# Adenosylhomocysteinase Tyrosine-protein kinase SRC
Massively Multitask Networks for Drug Discovery | 1502.02072#64 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 64,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "other enzyme transcription fac- tor transcription fac- tor transcription fac- tor transcription fac- tor other enzyme other enzyme other enzyme other enzyme protease protein kinase transcription fac- tor other enzyme protein kinase\n# dude-ptn1 dude-pur2 dude-pygm dude-pyrd dude-reni dude-rock1 dude-rxra\n130 50 77 111 104 100 131\n7250 2698 3948 6450 6958 6299 6948\nProtein-tyrosine phosphatase 1B GAR transformylase Muscle glycogen phosphorylase Dihydroorotate dehydrogenase Renin Rho-associated protein kinase 1 Retinoid X receptor alpha\n# dude-sahh dude-src\n63 524\n3450 34 491\n# Adenosylhomocysteinase Tyrosine-protein kinase SRC\nMassively Multitask Networks for Drug Discovery",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 65 | 63 524
3450 34 491
# Adenosylhomocysteinase Tyrosine-protein kinase SRC
Massively Multitask Networks for Drug Discovery
Dataset dude-tgfr1 dude-thb dude-thrb dude-try1 dude-tryb1 dude-tysy dude-urok dude-vgfr2 dude-wee1 dude-xiap Actives Inactives Target Class Target 133 103 461 449 148 109 162 409 102 8500 7448 26 999 25 967 7648 6748 9850 24 946 6150 other receptor transcription fac- tor protease protease protease other enzyme protease other receptor protein kinase TGF-beta receptor type I Thyroid hormone receptor beta-1 Thrombin Trypsin I Tryptase beta-1 Thymidylate synthase Urokinase-type plasminogen acti- vator Vascular endothelial growth factor receptor 2 Serine/threonine-protein WEE1 Inhibitor of apoptosis protein 3 kinase 100 5149 miscellaneous
Table A.2. Featurization failures.
Group Original Featurized Failure Rate (%) 439 879 PCBA DUD-E 1 200 966 95 916 MUV 11 764 Tox21 437 928 1 200 406 95 899 7830 0.44 0.05 0.02 33.44 | 1502.02072#65 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 65,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "63 524\n3450 34 491\n# Adenosylhomocysteinase Tyrosine-protein kinase SRC\nMassively Multitask Networks for Drug Discovery\nDataset dude-tgfr1 dude-thb dude-thrb dude-try1 dude-tryb1 dude-tysy dude-urok dude-vgfr2 dude-wee1 dude-xiap Actives Inactives Target Class Target 133 103 461 449 148 109 162 409 102 8500 7448 26 999 25 967 7648 6748 9850 24 946 6150 other receptor transcription fac- tor protease protease protease other enzyme protease other receptor protein kinase TGF-beta receptor type I Thyroid hormone receptor beta-1 Thrombin Trypsin I Tryptase beta-1 Thymidylate synthase Urokinase-type plasminogen acti- vator Vascular endothelial growth factor receptor 2 Serine/threonine-protein WEE1 Inhibitor of apoptosis protein 3 kinase 100 5149 miscellaneous\nTable A.2. Featurization failures.\nGroup Original Featurized Failure Rate (%) 439 879 PCBA DUD-E 1 200 966 95 916 MUV 11 764 Tox21 437 928 1 200 406 95 899 7830 0.44 0.05 0.02 33.44",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
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] |
1502.02072 | 66 | Massively Multitask Networks for Drug Discovery
Count [a DUD-E Mi Tox21 ME MUV Ma ~PCBA 40 30 20 : a = 0 a a | | | oo ce ce ot ZB os Oy oo es oF oi © yar gs* eo xe ee * gg aw a xo Ho gh ja? on on yo® oe ® g o fe) a? s ont 38 io Target Class
Figure A.1. Target class breakdown. Classes with fewer than ï¬ve members were merged into the âmiscellaneousâ class.
Massively Multitask Networks for Drug Discovery
Table A.3. Held-in datasets. | 1502.02072#66 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 66,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "Massively Multitask Networks for Drug Discovery\nCount [a DUD-E Mi Tox21 ME MUV Ma ~PCBA 40 30 20 : a = 0 a a | | | oo ce ce ot ZB os Oy oo es oF oi © yar gs* eo xe ee * gg aw a xo Ho gh ja? on on yo® oe ® g o fe) a? s ont 38 io Target Class\nFigure A.1. Target class breakdown. Classes with fewer than ï¬ve members were merged into the âmiscellaneousâ class.\nMassively Multitask Networks for Drug Discovery\nTable A.3. Held-in datasets.",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 67 | Massively Multitask Networks for Drug Discovery
Table A.3. Held-in datasets.
Dataset pcba-aid899 pcba-aid485297 pcba-aid651644 pcba-aid651768 pcba-aid743266 muv-aid466 muv-aid852 muv-aid859 tox-NR-Aromatase tox-SR-MMP Actives Inactives Target Class Target 1809 9126 748 1677 306 30 30 30 300 919 7575 311 481 361 115 362 320 405 368 14 999 15 000 15 000 5521 4891 other enzyme promoter miscellaneous Vpr other enzyme WRN GPCR GPCR protease GPCR other enzyme miscellaneous mitochondrial membrane potential CYP2C19 Rab9 PTHR1 S1P1 receptor FXIIa M1 receptor Aromatase
Table A.4. Held-out datasets. | 1502.02072#67 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 67,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "Massively Multitask Networks for Drug Discovery\nTable A.3. Held-in datasets.\nDataset pcba-aid899 pcba-aid485297 pcba-aid651644 pcba-aid651768 pcba-aid743266 muv-aid466 muv-aid852 muv-aid859 tox-NR-Aromatase tox-SR-MMP Actives Inactives Target Class Target 1809 9126 748 1677 306 30 30 30 300 919 7575 311 481 361 115 362 320 405 368 14 999 15 000 15 000 5521 4891 other enzyme promoter miscellaneous Vpr other enzyme WRN GPCR GPCR protease GPCR other enzyme miscellaneous mitochondrial membrane potential CYP2C19 Rab9 PTHR1 S1P1 receptor FXIIa M1 receptor Aromatase\nTable A.4. Held-out datasets.",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 68 | Table A.4. Held-out datasets.
Dataset pcba-aid1461 pcba-aid2675 pcba-aid602233 pcba-aid624417 pcba-aid652106 muv-aid548 muv-aid832 muv-aid846 tox-NR-AhR tox-SR-ATAD5 Actives Inactives Target Class Target 2305 99 165 6388 496 30 30 30 768 264 218 561 279 333 380 904 398 731 368 281 15 000 15 000 15 000 5780 6807 NPSR GPCR MBNL1-CUG miscellaneous PGK other enzyme GLP-1 GPCR alpha-synuclein miscellaneous PKA protein kinase Cathepsin G protease protease FXIa transcription factor Aryl hydrocarbon receptor promoter ATAD5
Massively Multitask Networks for Drug Discovery
PCBA MUV Tox21 DUD-E 0.8 0.6 m > 5 a * a Sj fs} 0.4 0.2 0.0
Figure A.2. Pairwise dataset intersections. The value of the element at position (x, y) corresponds to the fraction of dataset x that is contained in dataset y. Thin black lines are used to indicate divisions between dataset groups.
Massively Multitask Networks for Drug Discovery | 1502.02072#68 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 68,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "Table A.4. Held-out datasets.\nDataset pcba-aid1461 pcba-aid2675 pcba-aid602233 pcba-aid624417 pcba-aid652106 muv-aid548 muv-aid832 muv-aid846 tox-NR-AhR tox-SR-ATAD5 Actives Inactives Target Class Target 2305 99 165 6388 496 30 30 30 768 264 218 561 279 333 380 904 398 731 368 281 15 000 15 000 15 000 5780 6807 NPSR GPCR MBNL1-CUG miscellaneous PGK other enzyme GLP-1 GPCR alpha-synuclein miscellaneous PKA protein kinase Cathepsin G protease protease FXIa transcription factor Aryl hydrocarbon receptor promoter ATAD5\nMassively Multitask Networks for Drug Discovery\nPCBA MUV Tox21 DUD-E 0.8 0.6 m > 5 a * a Sj fs} 0.4 0.2 0.0\nFigure A.2. Pairwise dataset intersections. The value of the element at position (x, y) corresponds to the fraction of dataset x that is contained in dataset y. Thin black lines are used to indicate divisions between dataset groups.\nMassively Multitask Networks for Drug Discovery",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 69 | Massively Multitask Networks for Drug Discovery
2.0 1.5 + 1.0 0.5 0.0 A Log-odds-mean-AUC Duplicate Unique
Figure A.3. Multitask performance of duplicate and unique targets. Outliers are omitted for clarity. Notches indicate a conï¬dence interval around the median, computed as ±1.57 à IQR/
Massively Multitask Networks for Drug Discovery
# B. Performance metrics
Table B.1. Sign test CIs for each group of datasets. Each model is compared to the Pyramidal (2000, 100) Multitask Neural Net, .25 Dropout model.
Model PCBA (n = 128) MUV (n = 17) Tox21 (n = 12) [.3, .11] [.05, .16] [.02, .10] [.05, .15] [.09, .21] [.05, .15] [.13, .53] [.00, .18] [.13, .53] [.13, .53] [.13, .53] [.22, .64] [.00, .24] [.14, .61] [.00, .24] [.00, .24] [.14, .61] [.01, .35] | 1502.02072#69 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 69,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "Massively Multitask Networks for Drug Discovery\n2.0 1.5 + 1.0 0.5 0.0 A Log-odds-mean-AUC Duplicate Unique\nFigure A.3. Multitask performance of duplicate and unique targets. Outliers are omitted for clarity. Notches indicate a conï¬dence interval around the median, computed as ±1.57 à IQR/\nMassively Multitask Networks for Drug Discovery\n# B. Performance metrics\nTable B.1. Sign test CIs for each group of datasets. Each model is compared to the Pyramidal (2000, 100) Multitask Neural Net, .25 Dropout model.\nModel PCBA (n = 128) MUV (n = 17) Tox21 (n = 12) [.3, .11] [.05, .16] [.02, .10] [.05, .15] [.09, .21] [.05, .15] [.13, .53] [.00, .18] [.13, .53] [.13, .53] [.13, .53] [.22, .64] [.00, .24] [.14, .61] [.00, .24] [.00, .24] [.14, .61] [.01, .35]",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 70 | Logistic Regression (LR) Random Forest (RF) Single-Task Neural Net (STNN) Pyramidal (2000, 100) STNN, .25 Dropout (PSTNN) Max{LR, RF, STNN, PSTNN} 1-Hidden (1200) Layer Multitask Neural Net (MTNN)
Table B.2. Enrichment scores for all models reported in Table 2. Each value is the median across the datasets in a group of the mean k-fold enrichment values. Enrichment is an alternate measure of model performance common in virtual drug screening. We use the âROC enrichmentâ deï¬nition from (Jain & Nicholls, 2008), but roughly enrichment is the factor better than random that a modelâs top X% predictions are. | 1502.02072#70 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 70,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "Logistic Regression (LR) Random Forest (RF) Single-Task Neural Net (STNN) Pyramidal (2000, 100) STNN, .25 Dropout (PSTNN) Max{LR, RF, STNN, PSTNN} 1-Hidden (1200) Layer Multitask Neural Net (MTNN)\nTable B.2. Enrichment scores for all models reported in Table 2. Each value is the median across the datasets in a group of the mean k-fold enrichment values. Enrichment is an alternate measure of model performance common in virtual drug screening. We use the âROC enrichmentâ deï¬nition from (Jain & Nicholls, 2008), but roughly enrichment is the factor better than random that a modelâs top X% predictions are.",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 71 | Model MUV 0.5% 1% 2% 5% 0.5% 1% 2% 5% 0.5% 1% 2% 5% PCBA Tox21 19.4 LR 40.0 RF 19.0 STNN 21.8 PSTNN MTNN 33.8 PMTNN 43.8 16.5 27.4 15.6 16.9 23.6 29.6 12.1 17.4 11.8 12.4 16.9 19.7 7.9 9.1 7.7 7.9 9.8 11.2 20.0 40.0 26.7 26.7 26.7 40.0 23.3 26.7 20.0 16.7 16.7 23.3 15.0 16.7 11.7 13.3 16.7 16.7 8.0 7.3 8.0 8.0 8.7 10.0 23.9 23.2 16.2 23.8 24.5 23.5 18.3 19.5 14.4 16.1 18.0 18.5 10.6 13.6 9.8 10.0 11.4 13.7 6.7 7.8 6.1 6.7 6.9 8.1
Massively Multitask Networks for Drug Discovery | 1502.02072#71 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 71,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "Model MUV 0.5% 1% 2% 5% 0.5% 1% 2% 5% 0.5% 1% 2% 5% PCBA Tox21 19.4 LR 40.0 RF 19.0 STNN 21.8 PSTNN MTNN 33.8 PMTNN 43.8 16.5 27.4 15.6 16.9 23.6 29.6 12.1 17.4 11.8 12.4 16.9 19.7 7.9 9.1 7.7 7.9 9.8 11.2 20.0 40.0 26.7 26.7 26.7 40.0 23.3 26.7 20.0 16.7 16.7 23.3 15.0 16.7 11.7 13.3 16.7 16.7 8.0 7.3 8.0 8.0 8.7 10.0 23.9 23.2 16.2 23.8 24.5 23.5 18.3 19.5 14.4 16.1 18.0 18.5 10.6 13.6 9.8 10.0 11.4 13.7 6.7 7.8 6.1 6.7 6.9 8.1\nMassively Multitask Networks for Drug Discovery",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 72 | Massively Multitask Networks for Drug Discovery
40 PCBA 0.9 0.8 0.7 0.6 + 05 8 } i : : â | 0.4 MUV ¢ 10 a) = 09 oO to) © 08 3 2s 0.7 fe) . (5 06 & $05 oO = 04 40 Tox21 0.9 O7 t zk qT 06 7 i 0.5 0.4 gw « \) eats ys N) \ @ ow x oe? oo . Rw ao we P n ® a soeâ Wenâ >».
Figure B.1. Graphical representation of data from Table 2 in the text. Notches indicate a conï¬dence interval around the median, computed as ±1.57 à IQR/ N (McGill et al., 1978). Occasionally the notch limits go beyond the quartile markers, producing a âfolded downâ effect on the boxplot. Paired t-tests (2-sided) relative to the PMTNN across all non-DUD-E datasets gave p ⤠1.86 à 10â15.
Massively Multitask Networks for Drug Discovery
# C. Training Details | 1502.02072#72 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 72,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "Massively Multitask Networks for Drug Discovery\n40 PCBA 0.9 0.8 0.7 0.6 + 05 8 } i : : â | 0.4 MUV ¢ 10 a) = 09 oO to) © 08 3 2s 0.7 fe) . (5 06 & $05 oO = 04 40 Tox21 0.9 O7 t zk qT 06 7 i 0.5 0.4 gw « \\) eats ys N) \\ @ ow x oe? oo . Rw ao we P n ® a soeâ Wenâ >».\nFigure B.1. Graphical representation of data from Table 2 in the text. Notches indicate a conï¬dence interval around the median, computed as ±1.57 à IQR/ N (McGill et al., 1978). Occasionally the notch limits go beyond the quartile markers, producing a âfolded downâ effect on the boxplot. Paired t-tests (2-sided) relative to the PMTNN across all non-DUD-E datasets gave p ⤠1.86 à 10â15.\nMassively Multitask Networks for Drug Discovery\n# C. Training Details",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 73 | Massively Multitask Networks for Drug Discovery
# C. Training Details
The multitask networks in Table 2 were trained with learning rate .0003 and batch size 128 for 50M steps using stochastic gradient descent. Weights were initialized from a zero-mean Gaussian with standard deviation .01. The bias was initialized at .5. We experimented with higher learning rates, but found that the pyramidal networks sometimes failed to train (the top hidden layer zeroed itself out). However, this effect vanished with the lower learning rate. Most of the models were trained with 64 simultaneous replicas sharing their gradient updates, but in some cases we used as many as 256.
The pyramidal single-task networks were trained with the same settings, but for 100K steps. The vanilla single-task networks were trained with learning rate .001 for 100K steps. The networks used in Figure 3 and Figure 4 were trained with learning rate 0.003 for 500 epochs plus a constant 3 million steps. The constant factor was introduced after we observed that the smaller multitask networks required more epochs than the larger networks to stabilize.
The networks in Figure 5 were trained with a Pyramidal (1000, 50) Single Task architecture (matching the networks in Figure 3). The weights were initialized with the weights from the networks represented in Figure 3 and then trained for 100K steps with a learning rate of 0.0003. | 1502.02072#73 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 73,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "Massively Multitask Networks for Drug Discovery\n# C. Training Details\nThe multitask networks in Table 2 were trained with learning rate .0003 and batch size 128 for 50M steps using stochastic gradient descent. Weights were initialized from a zero-mean Gaussian with standard deviation .01. The bias was initialized at .5. We experimented with higher learning rates, but found that the pyramidal networks sometimes failed to train (the top hidden layer zeroed itself out). However, this effect vanished with the lower learning rate. Most of the models were trained with 64 simultaneous replicas sharing their gradient updates, but in some cases we used as many as 256.\nThe pyramidal single-task networks were trained with the same settings, but for 100K steps. The vanilla single-task networks were trained with learning rate .001 for 100K steps. The networks used in Figure 3 and Figure 4 were trained with learning rate 0.003 for 500 epochs plus a constant 3 million steps. The constant factor was introduced after we observed that the smaller multitask networks required more epochs than the larger networks to stabilize.\nThe networks in Figure 5 were trained with a Pyramidal (1000, 50) Single Task architecture (matching the networks in Figure 3). The weights were initialized with the weights from the networks represented in Figure 3 and then trained for 100K steps with a learning rate of 0.0003.",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
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] |
1502.02072 | 74 | As we noted in the main text, the datasets in our collection contained many more inactive than active compounds. To ensure the actives were given adequate importance during training, we weighted the actives for each dataset to have total weight equal to the number of inactives for that dataset (inactives were given unit weight).
Table C.1 contains the results of our pyramidal model sensitivity analysis. Tables C.2 and C.3 give results for a variety of additional models not reported in Table 2.
Table C.1. Pyramid sensitivity analysis. Median 5-fold-average-AUC values are given for several variations of the pyramidal architec- ture. In an attempt to avoid the problem of training failures due to the top layer becoming all zero early in the training, the learning rate was set to 0.0001 for the ï¬rst 2M steps then to 0.0003 for 28M steps. | 1502.02072#74 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 74,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "As we noted in the main text, the datasets in our collection contained many more inactive than active compounds. To ensure the actives were given adequate importance during training, we weighted the actives for each dataset to have total weight equal to the number of inactives for that dataset (inactives were given unit weight).\nTable C.1 contains the results of our pyramidal model sensitivity analysis. Tables C.2 and C.3 give results for a variety of additional models not reported in Table 2.\nTable C.1. Pyramid sensitivity analysis. Median 5-fold-average-AUC values are given for several variations of the pyramidal architec- ture. In an attempt to avoid the problem of training failures due to the top layer becoming all zero early in the training, the learning rate was set to 0.0001 for the ï¬rst 2M steps then to 0.0003 for 28M steps.",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 75 | Model PCBA (n = 128) MUV (n = 17) Tox21 (n = 12) Pyramidal (1000, 50) MTNN Pyramidal (1000, 100) MTNN Pyramidal (1000, 150) MTNN Pyramidal (2000, 50) MTNN Pyramidal (2000, 100) MTNN Pyramidal (2000, 150) MTNN Pyramidal (3000, 50) MTNN Pyramidal (3000, 100) MTNN Pyramidal (3000, 150) MTNN .846 .845 .842 .846 .846 .845 .848 .844 .843 .825 .818 .812 .819 .821 .839 .801 .804 .810 .799 .796 .798 .794 .798 .792 .796 .799 .789
Massively Multitask Networks for Drug Discovery
Table C.2. Descriptions for additional models. MTNN: multitask neural net. âAuxiliary headsâ refers to the attachment of independent softmax units for each task to hidden layers (see Szegedy et al., 2014). Unless otherwise marked, assume 10M training steps. | 1502.02072#75 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 75,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "Model PCBA (n = 128) MUV (n = 17) Tox21 (n = 12) Pyramidal (1000, 50) MTNN Pyramidal (1000, 100) MTNN Pyramidal (1000, 150) MTNN Pyramidal (2000, 50) MTNN Pyramidal (2000, 100) MTNN Pyramidal (2000, 150) MTNN Pyramidal (3000, 50) MTNN Pyramidal (3000, 100) MTNN Pyramidal (3000, 150) MTNN .846 .845 .842 .846 .846 .845 .848 .844 .843 .825 .818 .812 .819 .821 .839 .801 .804 .810 .799 .796 .798 .794 .798 .792 .796 .799 .789\nMassively Multitask Networks for Drug Discovery\nTable C.2. Descriptions for additional models. MTNN: multitask neural net. âAuxiliary headsâ refers to the attachment of independent softmax units for each task to hidden layers (see Szegedy et al., 2014). Unless otherwise marked, assume 10M training steps.",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 76 | A 8-Hidden (300) Layer MTNN, auxiliary heads attached to hidden layers 3 and 6, 6M steps B 1-Hidden (3000) Layer MTNN, 1M steps C 1-Hidden (3000) Layer MTNN, 1.5M steps D Pyramidal (1800, 100), 2 deep, reconnected (original input concatenated to ï¬rst pyramid output) E Pyramidal (1800, 100), 3 deep F G Pyramidal (2000, 100) MTNN, 10% connected H Pyramidal (2000, 100) MTNN, 50% connected I J K Pyramidal (2000, 100) MTNN, .25 Dropout (ï¬rst layer only), 50M steps L Pyramidal (2000, 100) MTNN, .25 Dropout, .001 learning rate
4-Hidden (1000) Layer MTNN, auxiliary heads attached to hidden layer 2, 4.5M steps
Pyramidal (2000, 100) MTNN, .001 learning rate Pyramidal (2000, 100) MTNN, 50M steps, .0003 learning rate
Table C.3. Median 5-fold-average AUC values for additional models. Sign test conï¬dence intervals and paired t-test (2-sided) p-values are relative to the PMTNN from Table 2 and were calculated across all non-DUD-E datasets. | 1502.02072#76 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 76,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "A 8-Hidden (300) Layer MTNN, auxiliary heads attached to hidden layers 3 and 6, 6M steps B 1-Hidden (3000) Layer MTNN, 1M steps C 1-Hidden (3000) Layer MTNN, 1.5M steps D Pyramidal (1800, 100), 2 deep, reconnected (original input concatenated to ï¬rst pyramid output) E Pyramidal (1800, 100), 3 deep F G Pyramidal (2000, 100) MTNN, 10% connected H Pyramidal (2000, 100) MTNN, 50% connected I J K Pyramidal (2000, 100) MTNN, .25 Dropout (ï¬rst layer only), 50M steps L Pyramidal (2000, 100) MTNN, .25 Dropout, .001 learning rate\n4-Hidden (1000) Layer MTNN, auxiliary heads attached to hidden layer 2, 4.5M steps\nPyramidal (2000, 100) MTNN, .001 learning rate Pyramidal (2000, 100) MTNN, 50M steps, .0003 learning rate\nTable C.3. Median 5-fold-average AUC values for additional models. Sign test conï¬dence intervals and paired t-test (2-sided) p-values are relative to the PMTNN from Table 2 and were calculated across all non-DUD-E datasets.",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 77 | Model PCBA (n = 128) MUV (n = 17) Tox21 (n = 12) Sign Test CI Paired t-Test A B C D E F G H I J K L .836 .835 .837 .842 .842 .858 .831 .856 .860 .830 .859 .872 .793 .855 .851 .842 .808 .836 .795 .827 .862 .810 .843 .837 .786 .769 .765 .816 .789 .810 .774 .796 .824 .801 .803 .802 [.01, .06] [.11, .22] [.12, .24] [.08, .18] [.02, .08] [.10, .22] [.03, .11] [.04, .13] [.07, .17] [.05, .14] [.24, .38] [.35, .50] 9.37 Ã 10â43 1.17 Ã 10â17 2.60 Ã 10â16 1.89 Ã 10â21 9.25 Ã 10â43 4.85 Ã 10â13 1.15 Ã 10â31 5.34 Ã 10â21 6.23 Ã 10â14 9.25 Ã 10â25 3.25 Ã 10â9 2.74 | 1502.02072#77 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 77,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "Model PCBA (n = 128) MUV (n = 17) Tox21 (n = 12) Sign Test CI Paired t-Test A B C D E F G H I J K L .836 .835 .837 .842 .842 .858 .831 .856 .860 .830 .859 .872 .793 .855 .851 .842 .808 .836 .795 .827 .862 .810 .843 .837 .786 .769 .765 .816 .789 .810 .774 .796 .824 .801 .803 .802 [.01, .06] [.11, .22] [.12, .24] [.08, .18] [.02, .08] [.10, .22] [.03, .11] [.04, .13] [.07, .17] [.05, .14] [.24, .38] [.35, .50] 9.37 Ã 10â43 1.17 Ã 10â17 2.60 Ã 10â16 1.89 Ã 10â21 9.25 Ã 10â43 4.85 Ã 10â13 1.15 Ã 10â31 5.34 Ã 10â21 6.23 Ã 10â14 9.25 Ã 10â25 3.25 Ã 10â9 2.74",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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] |
1502.02072 | 79 | Massively Multitask Networks for Drug Discovery
# References
Jain, Ajay N and Nicholls, Anthony. Recommendations for evaluation of computational methods. Journal of computer- aided molecular design, 22(3-4):133â139, 2008.
McGill, Robert, Tukey, John W, and Larsen, Wayne A. Variations of box plots. The American Statistician, 32(1):12â16, 1978.
Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Dumitru, Van- houcke, Vincent, and Rabinovich, Andrew. Going deeper with convolutions. arXiv preprint arXiv:1409.4842, 2014. | 1502.02072#79 | Massively Multitask Networks for Drug Discovery | Massively multitask neural architectures provide a learning framework for
drug discovery that synthesizes information from many distinct biological
sources. To train these architectures at scale, we gather large amounts of data
from public sources to create a dataset of nearly 40 million measurements
across more than 200 biological targets. We investigate several aspects of the
multitask framework by performing a series of empirical studies and obtain some
interesting results: (1) massively multitask networks obtain predictive
accuracies significantly better than single-task methods, (2) the predictive
power of multitask networks improves as additional tasks and data are added,
(3) the total amount of data and the total number of tasks both contribute
significantly to multitask improvement, and (4) multitask networks afford
limited transferability to tasks not in the training set. Our results
underscore the need for greater data sharing and further algorithmic innovation
to accelerate the drug discovery process. | http://arxiv.org/pdf/1502.02072 | Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande | stat.ML, cs.LG, cs.NE | Preliminary work. Under review by the International Conference on
Machine Learning (ICML) | null | stat.ML | 20150206 | 20150206 | [] | {
"authors": "Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande",
"chunk_id": 79,
"doc_id": "1502.02072",
"primary_category": "stat.ML",
"published": 20150206,
"source": "http://arxiv.org/pdf/1502.02072",
"summary": "Massively multitask neural architectures provide a learning framework for\ndrug discovery that synthesizes information from many distinct biological\nsources. To train these architectures at scale, we gather large amounts of data\nfrom public sources to create a dataset of nearly 40 million measurements\nacross more than 200 biological targets. We investigate several aspects of the\nmultitask framework by performing a series of empirical studies and obtain some\ninteresting results: (1) massively multitask networks obtain predictive\naccuracies significantly better than single-task methods, (2) the predictive\npower of multitask networks improves as additional tasks and data are added,\n(3) the total amount of data and the total number of tasks both contribute\nsignificantly to multitask improvement, and (4) multitask networks afford\nlimited transferability to tasks not in the training set. Our results\nunderscore the need for greater data sharing and further algorithmic innovation\nto accelerate the drug discovery process.",
"text": "Massively Multitask Networks for Drug Discovery\n# References\nJain, Ajay N and Nicholls, Anthony. Recommendations for evaluation of computational methods. Journal of computer- aided molecular design, 22(3-4):133â139, 2008.\nMcGill, Robert, Tukey, John W, and Larsen, Wayne A. Variations of box plots. The American Statistician, 32(1):12â16, 1978.\nSzegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Dumitru, Van- houcke, Vincent, and Rabinovich, Andrew. Going deeper with convolutions. arXiv preprint arXiv:1409.4842, 2014.",
"title": "Massively Multitask Networks for Drug Discovery",
"year": 2015
} | [
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1502.04623 | 22 | where wt is the N . For colour images each point in the input and error image (and hence in the reading and writing patches) is an RGB triple. In this case the same reading and writing ï¬lters are used for all three channels.
# 4. Experimental Results
Our model consists of an LSTM recurrent network that re- 12 âglimpseâ from the input image at each ceives a 12 time-step, using the selective read operation deï¬ned in Sec- tion 3.2. After a ï¬xed number of glimpses the network uses a softmax layer to classify the MNIST digit. The network is similar to the recently introduced Recurrent Attention Model (RAM) (Mnih et al., 2014), except that our attention method is differentiable; we therefore refer to it as âDiffer- entiable RAMâ.
We assess the ability of DRAW to generate realistic- looking images by training on three datasets of progres- sively increasing visual complexity: MNIST (LeCun et al., 1998), Street View House Numbers (SVHN) (Netzer et al., 2011) and CIFAR-10 (Krizhevsky, 2009). The images
The results in Table 1 demonstrate a signiï¬cant improve- ment in test error over the original RAM network. More- over our model had only a single attention patch at each | 1502.04623#22 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 22,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "where wt is the N . For colour images each point in the input and error image (and hence in the reading and writing patches) is an RGB triple. In this case the same reading and writing ï¬lters are used for all three channels.\n# 4. Experimental Results\nOur model consists of an LSTM recurrent network that re- 12 âglimpseâ from the input image at each ceives a 12 time-step, using the selective read operation deï¬ned in Sec- tion 3.2. After a ï¬xed number of glimpses the network uses a softmax layer to classify the MNIST digit. The network is similar to the recently introduced Recurrent Attention Model (RAM) (Mnih et al., 2014), except that our attention method is differentiable; we therefore refer to it as âDiffer- entiable RAMâ.\nWe assess the ability of DRAW to generate realistic- looking images by training on three datasets of progres- sively increasing visual complexity: MNIST (LeCun et al., 1998), Street View House Numbers (SVHN) (Netzer et al., 2011) and CIFAR-10 (Krizhevsky, 2009). The images\nThe results in Table 1 demonstrate a signiï¬cant improve- ment in test error over the original RAM network. More- over our model had only a single attention patch at each",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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] |
1502.04623 | 24 | Time â>
Figure 5. Cluttered MNIST classiï¬cation with attention. Each sequence shows a succession of four glimpses taken by the net- work while classifying cluttered translated MNIST. The green rectangle indicates the size and location of the attention patch, while the line width represents the variance of the ï¬lters.
Table 1. Classiï¬cation test error on 100 à 100 Cluttered Trans- lated MNIST.
Model Convolutional, 2 layers 12, 4 scales RAM, 4 glimpses, 12 RAM, 8 glimpses, 12 12, 4 scales Differentiable RAM, 4 glimpses, 12 Differentiable RAM, 8 glimpses, 12 Ã Ã 12 12 Error 14.35% 9.41% 8.11% 4.18% 3.36%
à Ã
time-step, whereas RAM used four, at different zooms.
# 4.2. MNIST Generation | 1502.04623#24 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 24,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "Time â>\nFigure 5. Cluttered MNIST classiï¬cation with attention. Each sequence shows a succession of four glimpses taken by the net- work while classifying cluttered translated MNIST. The green rectangle indicates the size and location of the attention patch, while the line width represents the variance of the ï¬lters.\nTable 1. Classiï¬cation test error on 100 à 100 Cluttered Trans- lated MNIST.\nModel Convolutional, 2 layers 12, 4 scales RAM, 4 glimpses, 12 RAM, 8 glimpses, 12 12, 4 scales Differentiable RAM, 4 glimpses, 12 Differentiable RAM, 8 glimpses, 12 à à 12 12 Error 14.35% 9.41% 8.11% 4.18% 3.36%\nà Ã\ntime-step, whereas RAM used four, at different zooms.\n# 4.2. MNIST Generation",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
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] |
1502.04623 | 25 | Ã Ã
time-step, whereas RAM used four, at different zooms.
# 4.2. MNIST Generation
Table 2. Negative log-likelihood (in nats) per test-set example on the binarised MNIST data set. The right hand column, where present, gives an upper bound (Eq. 12) on the negative log- likelihood. The previous results are from [1] (Salakhutdinov & Hinton, 2009), [2] (Murray & Salakhutdinov, 2009), [3] (Uria et al., 2014), [4] (Raiko et al., 2014), [5] (Rezende et al., 2014), [6] (Salimans et al., 2014), [7] (Gregor et al., 2014). | 1502.04623#25 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 25,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "Ã Ã\ntime-step, whereas RAM used four, at different zooms.\n# 4.2. MNIST Generation\nTable 2. Negative log-likelihood (in nats) per test-set example on the binarised MNIST data set. The right hand column, where present, gives an upper bound (Eq. 12) on the negative log- likelihood. The previous results are from [1] (Salakhutdinov & Hinton, 2009), [2] (Murray & Salakhutdinov, 2009), [3] (Uria et al., 2014), [4] (Raiko et al., 2014), [5] (Rezende et al., 2014), [6] (Salimans et al., 2014), [7] (Gregor et al., 2014).",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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] |
1502.04623 | 26 | Model DBM 2hl [1] DBN 2hl [2] NADE [3] EoNADE 2hl (128 orderings) [3] EoNADE-5 2hl (128 orderings) [4] DLGM [5] DLGM 8 leapfrog steps [6] DARN 1hl [7] DARN 12hl [7] DRAW without attention DRAW log p 84.62 84.55 â â â 88.33 85.10 84.68 â â â 86.60 85.51 84.13 - - - ⤠88.30 88.30 87.72 87.40 80.97
3 3 g / g 7 \ 7 t SHWe YP HOLA VEQNROQALHOD PoepIvYyleqy NEDA STUN ~~âwt JI ncnw-~s AK~DNPA LL Ooms SS Bo Ro 4DYAXSOZAIF O33 {5 \ 4O* ¢ OH 19Q 373 ! S70 SgS Z
Figure 6. Generated MNIST images. All digits were generated by DRAW except those in the rightmost column, which shows the training set images closest to those in the column second to the right (pixelwise L2 is the distance measure). Note that the net- work was trained on binary samples, while the generated images are mean probabilities. | 1502.04623#26 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 26,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "Model DBM 2hl [1] DBN 2hl [2] NADE [3] EoNADE 2hl (128 orderings) [3] EoNADE-5 2hl (128 orderings) [4] DLGM [5] DLGM 8 leapfrog steps [6] DARN 1hl [7] DARN 12hl [7] DRAW without attention DRAW log p 84.62 84.55 â â â 88.33 85.10 84.68 â â â 86.60 85.51 84.13 - - - ⤠88.30 88.30 87.72 87.40 80.97\n3 3 g / g 7 \\ 7 t SHWe YP HOLA VEQNROQALHOD PoepIvYyleqy NEDA STUN ~~âwt JI ncnw-~s AK~DNPA LL Ooms SS Bo Ro 4DYAXSOZAIF O33 {5 \\ 4O* ¢ OH 19Q 373 !\u0002 S70 SgS Z\nFigure 6. Generated MNIST images. All digits were generated by DRAW except those in the rightmost column, which shows the training set images closest to those in the column second to the right (pixelwise L2 is the distance measure). Note that the net- work was trained on binary samples, while the generated images are mean probabilities.",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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] |
1502.04623 | 27 | We trained the full DRAW network as a generative model on the binarized MNIST dataset (Salakhutdinov & Mur- ray, 2008). This dataset has been widely studied in the literature, allowing us to compare the numerical perfor- mance (measured in average nats per image on the test set) of DRAW with existing methods. Table 2 shows that DRAW without selective attention performs comparably to other recent generative models such as DARN, NADE and DBMs, and that DRAW with attention considerably im- proves on the state of the art.
Once the DRAW network was trained, we generated MNIST digits following the method in Section 2.3, exam- ples of which are presented in Fig. 6. Fig. 7 illustrates the image generation sequence for a DRAW network with- out selective attention (see Section 3.1). It is interesting to compare this with the generation sequence for DRAW with attention, as depicted in Fig. 1. Whereas without attention it progressively sharpens a blurred image in a global way,
DRAW: A Recurrent Neural Network For Image Generation
7 seisisssssiss
Time â~
Figure 7. MNIST generation sequences for DRAW without at- tention. Notice how the network ï¬rst generates a very blurry im- age that is subsequently reï¬ned. | 1502.04623#27 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 27,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "We trained the full DRAW network as a generative model on the binarized MNIST dataset (Salakhutdinov & Mur- ray, 2008). This dataset has been widely studied in the literature, allowing us to compare the numerical perfor- mance (measured in average nats per image on the test set) of DRAW with existing methods. Table 2 shows that DRAW without selective attention performs comparably to other recent generative models such as DARN, NADE and DBMs, and that DRAW with attention considerably im- proves on the state of the art.\nOnce the DRAW network was trained, we generated MNIST digits following the method in Section 2.3, exam- ples of which are presented in Fig. 6. Fig. 7 illustrates the image generation sequence for a DRAW network with- out selective attention (see Section 3.1). It is interesting to compare this with the generation sequence for DRAW with attention, as depicted in Fig. 1. Whereas without attention it progressively sharpens a blurred image in a global way,\nDRAW: A Recurrent Neural Network For Image Generation\n7 seisisssssiss\nTime â~\nFigure 7. MNIST generation sequences for DRAW without at- tention. Notice how the network ï¬rst generates a very blurry im- age that is subsequently reï¬ned.",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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] |
1502.04623 | 28 | Figure 8. Generated MNIST images with two digits.
with attention it constructs the digit by tracing the linesâ much like a person with a pen.
# 4.3. MNIST Generation with Two Digits
The main motivation for using an attention-based genera- tive model is that large images can be built up iteratively, by adding to a small part of the image at a time. To test this capability in a controlled fashion, we trained DRAW 28 MNIST images cho- to generate images with two 28 sen at random and placed at random locations in a 60 60 black background. In cases where the two digits overlap, the pixel intensities were added together at each point and clipped to be no greater than one. Examples of generated data are shown in Fig. 8. The network typically generates one digit and then the other, suggesting an ability to recre- ate composite scenes from simple pieces.
Fy âiste aii iI a
# 4.4. Street View House Number Generation | 1502.04623#28 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 28,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "Figure 8. Generated MNIST images with two digits.\nwith attention it constructs the digit by tracing the linesâ much like a person with a pen.\n# 4.3. MNIST Generation with Two Digits\nThe main motivation for using an attention-based genera- tive model is that large images can be built up iteratively, by adding to a small part of the image at a time. To test this capability in a controlled fashion, we trained DRAW 28 MNIST images cho- to generate images with two 28 sen at random and placed at random locations in a 60 60 black background. In cases where the two digits overlap, the pixel intensities were added together at each point and clipped to be no greater than one. Examples of generated data are shown in Fig. 8. The network typically generates one digit and then the other, suggesting an ability to recre- ate composite scenes from simple pieces.\nFy âiste aii iI a\n# 4.4. Street View House Number Generation",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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] |
1502.04623 | 29 | Fy âiste aii iI a
# 4.4. Street View House Number Generation
MNIST digits are very simplistic in terms of visual struc- ture, and we were keen to see how well DRAW performed on natural images. Our ï¬rst natural image generation ex- periment used the multi-digit Street View House Numbers dataset (Netzer et al., 2011). We used the same preprocess- 64 house ing as (Goodfellow et al., 2013), yielding a 64 number image for each training example. The network was then trained using 54 54 patches extracted at random lo- cations from the preprocessed images. The SVHN training set contains 231,053 images, and the validation set contains 4,701 images.
Figure 9. Generated SVHN images. The rightmost column shows the training images closest (in L2 distance) to the gener- ated images beside them. Note that the two columns are visually similar, but the numbers are generally different.
highly realistic, as shown in Figs. 9 and 10. Fig. 11 reveals that, despite the long training time, the DRAW network un- derï¬t the SVHN training data.
# 4.5. Generating CIFAR Images
The house number images generated by the network are
The most challenging dataset we applied DRAW to was the CIFAR-10 collection of natural images (Krizhevsky, | 1502.04623#29 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 29,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "Fy âiste aii iI a\n# 4.4. Street View House Number Generation\nMNIST digits are very simplistic in terms of visual struc- ture, and we were keen to see how well DRAW performed on natural images. Our ï¬rst natural image generation ex- periment used the multi-digit Street View House Numbers dataset (Netzer et al., 2011). We used the same preprocess- 64 house ing as (Goodfellow et al., 2013), yielding a 64 number image for each training example. The network was then trained using 54 54 patches extracted at random lo- cations from the preprocessed images. The SVHN training set contains 231,053 images, and the validation set contains 4,701 images.\nFigure 9. Generated SVHN images. The rightmost column shows the training images closest (in L2 distance) to the gener- ated images beside them. Note that the two columns are visually similar, but the numbers are generally different.\nhighly realistic, as shown in Figs. 9 and 10. Fig. 11 reveals that, despite the long training time, the DRAW network un- derï¬t the SVHN training data.\n# 4.5. Generating CIFAR Images\nThe house number images generated by the network are\nThe most challenging dataset we applied DRAW to was the CIFAR-10 collection of natural images (Krizhevsky,",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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] |
1502.04623 | 30 | The house number images generated by the network are
The most challenging dataset we applied DRAW to was the CIFAR-10 collection of natural images (Krizhevsky,
DRAW: A Recurrent Neural Network For Image Generation
# s
Table 3. Experimental Hyper-Parameters. Task 100 MNIST Model SVHN Model CIFAR Model 100 MNIST Classiï¬cation à #glimpses LSTM #h 256 256 800 400 8 64 32 64 #z Read Size Write Size 12 2 12 5 - 100 100 200 - 12 2 12 5 5 12 5 5 12 5
à à à Ã
à à Ã
Ld) 3) 10) S70] BRieeiaan am 71 Tm
Pe Zn ee mea
Time â>
Figure 10. SVHN Generation Sequences. The red rectangle in- dicates the attention patch. Notice how the network draws the dig- its one at a time, and how it moves and scales the writing patch to produce numbers with different slopes and sizes.
5220 5200 5180 5160 5140 5120 5100 5080 5060 training â+â validation â=»â cost per example 0 50 100 150 200 250 minibatch number (thousands) 300 350
Figure 12. Generated CIFAR images. The rightmost column shows the nearest training examples to the column beside it. | 1502.04623#30 | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | http://arxiv.org/pdf/1502.04623 | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra | cs.CV, cs.LG, cs.NE | null | null | cs.CV | 20150216 | 20150520 | [] | {
"authors": "Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra",
"chunk_id": 30,
"doc_id": "1502.04623",
"primary_category": "cs.CV",
"published": 20150216,
"source": "http://arxiv.org/pdf/1502.04623",
"summary": "This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural\nnetwork architecture for image generation. DRAW networks combine a novel\nspatial attention mechanism that mimics the foveation of the human eye, with a\nsequential variational auto-encoding framework that allows for the iterative\nconstruction of complex images. The system substantially improves on the state\nof the art for generative models on MNIST, and, when trained on the Street View\nHouse Numbers dataset, it generates images that cannot be distinguished from\nreal data with the naked eye.",
"text": "The house number images generated by the network are\nThe most challenging dataset we applied DRAW to was the CIFAR-10 collection of natural images (Krizhevsky,\nDRAW: A Recurrent Neural Network For Image Generation\n# s\nTable 3. Experimental Hyper-Parameters. Task 100 MNIST Model SVHN Model CIFAR Model 100 MNIST Classiï¬cation à #glimpses LSTM #h 256 256 800 400 8 64 32 64 #z Read Size Write Size 12 2 12 5 - 100 100 200 - 12 2 12 5 5 12 5 5 12 5\nà à à Ã\nà à Ã\nLd) 3) 10) S70] BRieeiaan am 71 Tm\nPe Zn ee mea\nTime â>\nFigure 10. SVHN Generation Sequences. The red rectangle in- dicates the attention patch. Notice how the network draws the dig- its one at a time, and how it moves and scales the writing patch to produce numbers with different slopes and sizes.\n5220 5200 5180 5160 5140 5120 5100 5080 5060 training â+â validation â=»â cost per example 0 50 100 150 200 250 minibatch number (thousands) 300 350\nFigure 12. Generated CIFAR images. The rightmost column shows the nearest training examples to the column beside it.",
"title": "DRAW: A Recurrent Neural Network For Image Generation",
"year": 2015
} | [
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