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On the mapping between Hopfield networks and Restricted Boltzmann Machines | https://openreview.net/forum?id=RGJbergVIoO | https://openreview.net/forum?id=RGJbergVIoO | Matthew Smart,Anton Zilman | ICLR 2021,Oral | Hopfield networks (HNs) and Restricted Boltzmann Machines (RBMs) are two important models at the interface of statistical physics, machine learning, and neuroscience. Recently, there has been interest in the relationship between HNs and RBMs, due to their similarity under the statistical mechanics formalism. An exact mapping between HNs and RBMs has been previously noted for the special case of orthogonal (“uncorrelated”) encoded patterns. We present here an exact mapping in the case of correlated pattern HNs, which are more broadly applicable to existing datasets. Specifically, we show that any HN with $N$ binary variables and $p<N$ potentially correlated binary patterns can be transformed into an RBM with $N$ binary visible variables and $p$ gaussian hidden variables. We outline the conditions under which the reverse mapping exists, and conduct experiments on the MNIST dataset which suggest the mapping provides a useful initialization to the RBM weights. We discuss extensions, the potential importance of this correspondence for the training of RBMs, and for understanding the performance of feature extraction methods which utilize RBMs. | https://openreview.net/pdf/3a9204f4495810f86acf886d14ee022a31d7b863.pdf |
Complex Query Answering with Neural Link Predictors | https://openreview.net/forum?id=Mos9F9kDwkz | https://openreview.net/forum?id=Mos9F9kDwkz | Erik Arakelyan,Daniel Daza,Pasquale Minervini,Michael Cochez | ICLR 2021,Oral | Neural link predictors are immensely useful for identifying missing edges in large scale Knowledge Graphs. However, it is still not clear how to use these models for answering more complex queries that arise in a number of domains, such as queries using logical conjunctions ($\land$), disjunctions ($\lor$) and existential quantifiers ($\exists$), while accounting for missing edges. In this work, we propose a framework for efficiently answering complex queries on incomplete Knowledge Graphs. We translate each query into an end-to-end differentiable objective, where the truth value of each atom is computed by a pre-trained neural link predictor. We then analyse two solutions to the optimisation problem, including gradient-based and combinatorial search. In our experiments, the proposed approach produces more accurate results than state-of-the-art methods --- black-box neural models trained on millions of generated queries --- without the need of training on a large and diverse set of complex queries. Using orders of magnitude less training data, we obtain relative improvements ranging from 8% up to 40% in Hits@3 across different knowledge graphs containing factual information. Finally, we demonstrate that it is possible to explain the outcome of our model in terms of the intermediate solutions identified for each of the complex query atoms. All our source code and datasets are available online, at https://github.com/uclnlp/cqd. | https://openreview.net/pdf/f3977c5e4b8a00127c9aed2b62ae904f29f06744.pdf |
Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation | https://openreview.net/forum?id=Wj4ODo0uyCF | https://openreview.net/forum?id=Wj4ODo0uyCF | Biao Zhang,Ankur Bapna,Rico Sennrich,Orhan Firat | ICLR 2021,Oral | Using a mix of shared and language-specific (LS) parameters has shown promise in multilingual neural machine translation (MNMT), but the question of when and where LS capacity matters most is still under-studied. We offer such a study by proposing conditional language-specific routing (CLSR). CLSR employs hard binary gates conditioned on token representations to dynamically select LS or shared paths. By manipulating these gates, it can schedule LS capacity across sub-layers in MNMT subject to the guidance of translation signals and budget constraints. Moreover, CLSR can easily scale up to massively multilingual settings. Experiments with Transformer on OPUS-100 and WMT datasets show that: 1) MNMT is sensitive to both the amount and the position of LS modeling: distributing 10%-30% LS computation to the top and/or bottom encoder/decoder layers delivers the best performance; and 2) one-to-many translation benefits more from CLSR compared to many-to-one translation, particularly with unbalanced training data. Our study further verifies the trade-off between the shared capacity and LS capacity for multilingual translation. We corroborate our analysis by confirming the soundness of our findings as foundation of our improved multilingual Transformers. Source code and models are available at https://github.com/bzhangGo/zero/tree/iclr2021_clsr. | https://openreview.net/pdf/daf5088c43f0425f9ab145f2bb0b1db43092147f.pdf |
End-to-end Adversarial Text-to-Speech | https://openreview.net/forum?id=rsf1z-JSj87 | https://openreview.net/forum?id=rsf1z-JSj87 | Jeff Donahue,Sander Dieleman,Mikolaj Binkowski,Erich Elsen,Karen Simonyan | ICLR 2021,Oral | Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest. In this work, we take on the challenging task of learning to synthesise speech from normalised text or phonemes in an end-to-end manner, resulting in models which operate directly on character or phoneme input sequences and produce raw speech audio outputs. Our proposed generator is feed-forward and thus efficient for both training and inference, using a differentiable alignment scheme based on token length prediction. It learns to produce high fidelity audio through a combination of adversarial feedback and prediction losses constraining the generated audio to roughly match the ground truth in terms of its total duration and mel-spectrogram. To allow the model to capture temporal variation in the generated audio, we employ soft dynamic time warping in the spectrogram-based prediction loss. The resulting model achieves a mean opinion score exceeding 4 on a 5 point scale, which is comparable to the state-of-the-art models relying on multi-stage training and additional supervision. | https://openreview.net/pdf/37c14e59635044278b29d57254fbd09950d3d37a.pdf |
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity | https://openreview.net/forum?id=gvxJzw8kW4b | https://openreview.net/forum?id=gvxJzw8kW4b | JangHyun Kim,Wonho Choo,Hosan Jeong,Hyun Oh Song | ICLR 2021,Oral | While deep neural networks show great performance on fitting to the training distribution, improving the networks' generalization performance to the test distribution and robustness to the sensitivity to input perturbations still remain as a challenge. Although a number of mixup based augmentation strategies have been proposed to partially address them, it remains unclear as to how to best utilize the supervisory signal within each input data for mixup from the optimization perspective. We propose a new perspective on batch mixup and formulate the optimal construction of a batch of mixup data maximizing the data saliency measure of each individual mixup data and encouraging the supermodular diversity among the constructed mixup data. This leads to a novel discrete optimization problem minimizing the difference between submodular functions. We also propose an efficient modular approximation based iterative submodular minimization algorithm for efficient mixup computation per each minibatch suitable for minibatch based neural network training. Our experiments show the proposed method achieves the state of the art generalization, calibration, and weakly supervised localization results compared to other mixup methods. The source code is available at https://github.com/snu-mllab/Co-Mixup. | https://openreview.net/pdf/199727fa0bf10af61eddaed5e0c98ee9bbcfdea3.pdf |
Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions | https://openreview.net/forum?id=Ud3DSz72nYR | https://openreview.net/forum?id=Ud3DSz72nYR | Zhengxian Lin,Kin-Ho Lam,Alan Fern | ICLR 2021,Oral | We investigate a deep reinforcement learning (RL) architecture that supports explaining why a learned agent prefers one action over another. The key idea is to learn action-values that are directly represented via human-understandable properties of expected futures. This is realized via the embedded self-prediction (ESP) model, which learns said properties in terms of human provided features. Action preferences can then be explained by contrasting the future properties predicted for each action. To address cases where there are a large number of features, we develop a novel method for computing minimal sufficient explanations from an ESP. Our case studies in three domains, including a complex strategy game, show that ESP models can be effectively learned and support insightful explanations. | https://openreview.net/pdf/0b44de227203c9a6da82618d99fd47af97f88da6.pdf |
Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets? | https://openreview.net/forum?id=uCY5MuAxcxU | https://openreview.net/forum?id=uCY5MuAxcxU | Zhiyuan Li,Yi Zhang,Sanjeev Arora | ICLR 2021,Oral | Convolutional neural networks often dominate fully-connected counterparts in generalization performance, especially on image classification tasks. This is often explained in terms of \textquotedblleft better inductive bias.\textquotedblright\ However, this has not been made mathematically rigorous, and the hurdle is that the sufficiently wide fully-connected net can always simulate the convolutional net. Thus the training algorithm plays a role. The current work describes a natural task on which a provable sample complexity gap can be shown, for standard training algorithms. We construct a single natural distribution on $\mathbb{R}^d\times\{\pm 1\}$ on which any orthogonal-invariant algorithm (i.e. fully-connected networks trained with most gradient-based methods from gaussian initialization) requires $\Omega(d^2)$ samples to generalize while $O(1)$ samples suffice for convolutional architectures. Furthermore, we demonstrate a single target function, learning which on all possible distributions leads to an $O(1)$ vs $\Omega(d^2/\varepsilon)$ gap. The proof relies on the fact that SGD on fully-connected network is orthogonal equivariant. Similar results are achieved for $\ell_2$ regression and adaptive training algorithms, e.g. Adam and AdaGrad, which are only permutation equivariant. | https://openreview.net/pdf/349f4553ada8090561541dc0bcef8232a03b04ff.pdf |
Iterated learning for emergent systematicity in VQA | https://openreview.net/forum?id=Pd_oMxH8IlF | https://openreview.net/forum?id=Pd_oMxH8IlF | Ankit Vani,Max Schwarzer,Yuchen Lu,Eeshan Dhekane,Aaron Courville | ICLR 2021,Oral | Although neural module networks have an architectural bias towards compositionality, they require gold standard layouts to generalize systematically in practice. When instead learning layouts and modules jointly, compositionality does not arise automatically and an explicit pressure is necessary for the emergence of layouts exhibiting the right structure. We propose to address this problem using iterated learning, a cognitive science theory of the emergence of compositional languages in nature that has primarily been applied to simple referential games in machine learning. Considering the layouts of module networks as samples from an emergent language, we use iterated learning to encourage the development of structure within this language. We show that the resulting layouts support systematic generalization in neural agents solving the more complex task of visual question-answering. Our regularized iterated learning method can outperform baselines without iterated learning on SHAPES-SyGeT (SHAPES Systematic Generalization Test), a new split of the SHAPES dataset we introduce to evaluate systematic generalization, and on CLOSURE, an extension of CLEVR also designed to test systematic generalization. We demonstrate superior performance in recovering ground-truth compositional program structure with limited supervision on both SHAPES-SyGeT and CLEVR. | https://openreview.net/pdf/62bee9dfb73bae4271c7f80e9d64eda7effacc43.pdf |
When Do Curricula Work? | https://openreview.net/forum?id=tW4QEInpni | https://openreview.net/forum?id=tW4QEInpni | Xiaoxia Wu,Ethan Dyer,Behnam Neyshabur | ICLR 2021,Oral | Inspired by human learning, researchers have proposed ordering examples during training based on their difficulty. Both curriculum learning, exposing a network to easier examples early in training, and anti-curriculum learning, showing the most difficult examples first, have been suggested as improvements to the standard i.i.d. training. In this work, we set out to investigate the relative benefits of ordered learning. We first investigate the implicit curricula resulting from architectural and optimization bias and find that samples are learned in a highly consistent order. Next, to quantify the benefit of explicit curricula, we conduct extensive experiments over thousands of orderings spanning three kinds of learning: curriculum, anti-curriculum, and random-curriculum -- in which the size of the training dataset is dynamically increased over time, but the examples are randomly ordered. We find that for standard benchmark datasets, curricula have only marginal benefits, and that randomly ordered samples perform as well or better than curricula and anti-curricula, suggesting that any benefit is entirely due to the dynamic training set size. Inspired by common use cases of curriculum learning in practice, we investigate the role of limited training time budget and noisy data in the success of curriculum learning. Our experiments demonstrate that curriculum, but not anti-curriculum or random ordering can indeed improve the performance either with limited training time budget or in the existence of noisy data. | https://openreview.net/pdf/a6f2f483d8e768e936c0ab7b9c6f8209e4fb79a4.pdf |
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients | https://openreview.net/forum?id=m5Qsh0kBQG | https://openreview.net/forum?id=m5Qsh0kBQG | Brenden K Petersen,Mikel Landajuela Larma,Terrell N. Mundhenk,Claudio Prata Santiago,Soo Kyung Kim,Joanne Taery Kim | ICLR 2021,Oral | Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence. This is the problem of $\textit{symbolic regression}$. Despite recent advances in training neural networks to solve complex tasks, deep learning approaches to symbolic regression are underexplored. We propose a framework that leverages deep learning for symbolic regression via a simple idea: use a large model to search the space of small models. Specifically, we use a recurrent neural network to emit a distribution over tractable mathematical expressions and employ a novel risk-seeking policy gradient to train the network to generate better-fitting expressions. Our algorithm outperforms several baseline methods (including Eureqa, the gold standard for symbolic regression) in its ability to exactly recover symbolic expressions on a series of benchmark problems, both with and without added noise. More broadly, our contributions include a framework that can be applied to optimize hierarchical, variable-length objects under a black-box performance metric, with the ability to incorporate constraints in situ, and a risk-seeking policy gradient formulation that optimizes for best-case performance instead of expected performance. | https://openreview.net/pdf/317665469793748a5dbbedaa91f4f31e395d23bf.pdf |
Improved Autoregressive Modeling with Distribution Smoothing | https://openreview.net/forum?id=rJA5Pz7lHKb | https://openreview.net/forum?id=rJA5Pz7lHKb | Chenlin Meng,Jiaming Song,Yang Song,Shengjia Zhao,Stefano Ermon | ICLR 2021,Oral | While autoregressive models excel at image compression, their sample quality is often lacking. Although not realistic, generated images often have high likelihood according to the model, resembling the case of adversarial examples. Inspired by a successful adversarial defense method, we incorporate randomized smoothing into autoregressive generative modeling. We first model a smoothed version of the data distribution, and then reverse the smoothing process to recover the original data distribution. This procedure drastically improves the sample quality of existing autoregressive models on several synthetic and real-world image datasets while obtaining competitive likelihoods on synthetic datasets. | https://openreview.net/pdf/e633e1ad3eac717e6d9e931ddd05ee2e3c9031f3.pdf |
Score-Based Generative Modeling through Stochastic Differential Equations | https://openreview.net/forum?id=PxTIG12RRHS | https://openreview.net/forum?id=PxTIG12RRHS | Yang Song,Jascha Sohl-Dickstein,Diederik P Kingma,Abhishek Kumar,Stefano Ermon,Ben Poole | ICLR 2021,Oral | Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise.
Crucially, the reverse-time SDE depends only on the time-dependent gradient field (a.k.a., score) of the perturbed data distribution. By leveraging advances in score-based generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in score-based generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with score-based models, as demonstrated with experiments on class-conditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of $1024\times 1024$ images for the first time from a score-based generative model. | https://openreview.net/pdf/ef0eadbe07115b0853e964f17aa09d811cd490f1.pdf |
Global Convergence of Three-layer Neural Networks in the Mean Field Regime | https://openreview.net/forum?id=KvyxFqZS_D | https://openreview.net/forum?id=KvyxFqZS_D | Huy Tuan Pham,Phan-Minh Nguyen | ICLR 2021,Oral | In the mean field regime, neural networks are appropriately scaled so that as the width tends to infinity, the learning dynamics tends to a nonlinear and nontrivial dynamical limit, known as the mean field limit. This lends a way to study large-width neural networks via analyzing the mean field limit. Recent works have successfully applied such analysis to two-layer networks and provided global convergence guarantees. The extension to multilayer ones however has been a highly challenging puzzle, and little is known about the optimization efficiency in the mean field regime when there are more than two layers.
In this work, we prove a global convergence result for unregularized feedforward three-layer networks in the mean field regime. We first develop a rigorous framework to establish the mean field limit of three-layer networks under stochastic gradient descent training. To that end, we propose the idea of a neuronal embedding, which comprises of a fixed probability space that encapsulates neural networks of arbitrary sizes. The identified mean field limit is then used to prove a global convergence guarantee under suitable regularity and convergence mode assumptions, which – unlike previous works on two-layer networks – does not rely critically on convexity. Underlying the result is a universal approximation property, natural of neural networks, which importantly is shown to hold at any finite training time (not necessarily at convergence) via an algebraic topology argument. | https://openreview.net/pdf/459bd57a5e53f59b4f2736fed403a773b5494f55.pdf |
Rethinking Architecture Selection in Differentiable NAS | https://openreview.net/forum?id=PKubaeJkw3 | https://openreview.net/forum?id=PKubaeJkw3 | Ruochen Wang,Minhao Cheng,Xiangning Chen,Xiaocheng Tang,Cho-Jui Hsieh | ICLR 2021,Oral | Differentiable Neural Architecture Search is one of the most popular Neural Architecture Search (NAS) methods for its search efficiency and simplicity, accomplished by jointly optimizing the model weight and architecture parameters in a weight-sharing supernet via gradient-based algorithms. At the end of the search phase, the operations with the largest architecture parameters will be selected to form the final architecture, with the implicit assumption that the values of architecture parameters reflect the operation strength. While much has been discussed about the supernet's optimization, the architecture selection process has received little attention. We provide empirical and theoretical analysis to show that the magnitude of architecture parameters does not necessarily indicate how much the operation contributes to the supernet's performance. We propose an alternative perturbation-based architecture selection that directly measures each operation's influence on the supernet. We re-evaluate several differentiable NAS methods with the proposed architecture selection and find that it is able to extract significantly improved architectures from the underlying supernets consistently. Furthermore, we find that several failure modes of DARTS can be greatly alleviated with the proposed selection method, indicating that much of the poor generalization observed in DARTS can be attributed to the failure of magnitude-based architecture selection rather than entirely the optimization of its supernet. | https://openreview.net/pdf/aaf87608f6fe51a76f04c614f46d177673b5751c.pdf |
Evolving Reinforcement Learning Algorithms | https://openreview.net/forum?id=0XXpJ4OtjW | https://openreview.net/forum?id=0XXpJ4OtjW | John D Co-Reyes,Yingjie Miao,Daiyi Peng,Esteban Real,Quoc V Le,Sergey Levine,Honglak Lee,Aleksandra Faust | ICLR 2021,Oral | We propose a method for meta-learning reinforcement learning algorithms by searching over the space of computational graphs which compute the loss function for a value-based model-free RL agent to optimize. The learned algorithms are domain-agnostic and can generalize to new environments not seen during training. Our method can both learn from scratch and bootstrap off known existing algorithms, like DQN, enabling interpretable modifications which improve performance. Learning from scratch on simple classical control and gridworld tasks, our method rediscovers the temporal-difference (TD) algorithm. Bootstrapped from DQN, we highlight two learned algorithms which obtain good generalization performance over other classical control tasks, gridworld type tasks, and Atari games. The analysis of the learned algorithm behavior shows resemblance to recently proposed RL algorithms that address overestimation in value-based methods. | https://openreview.net/pdf/78e8fae1b2cfbbae3e7010ca2f27649cb057ae84.pdf |
Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering | https://openreview.net/forum?id=yWkP7JuHX1 | https://openreview.net/forum?id=yWkP7JuHX1 | Yuxuan Zhang,Wenzheng Chen,Huan Ling,Jun Gao,Yinan Zhang,Antonio Torralba,Sanja Fidler | ICLR 2021,Oral | Differentiable rendering has paved the way to training neural networks to perform “inverse graphics” tasks such as predicting 3D geometry from monocular photographs. To train high performing models, most of the current approaches rely on multi-view imagery which are not readily available in practice. Recent Generative Adversarial Networks (GANs) that synthesize images, in contrast, seem to acquire 3D knowledge implicitly during training: object viewpoints can be manipulated by simply manipulating the latent codes. However, these latent codes often lack further physical interpretation and thus GANs cannot easily be inverted to perform explicit 3D reasoning. In this paper, we aim to extract and disentangle 3D knowledge learned by generative models by utilizing differentiable renderers. Key to our approach is to exploit GANs as a multi-view data generator to train an inverse graphics network using an off-the-shelf differentiable renderer, and the trained inverse graphics network as a teacher to disentangle the GAN's latent code into interpretable 3D properties. The entire architecture is trained iteratively using cycle consistency losses. We show that our approach significantly outperforms state-of-the-art inverse graphics networks trained on existing datasets, both quantitatively and via user studies. We further showcase the disentangled GAN as a controllable 3D “neural renderer", complementing traditional graphics renderers. | https://openreview.net/pdf/ddea76d45b925517b5cf900df64b91dc3d44d918.pdf |
MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training | https://openreview.net/forum?id=wWK7yXkULyh | https://openreview.net/forum?id=wWK7yXkULyh | Beidi Chen,Zichang Liu,Binghui Peng,Zhaozhuo Xu,Jonathan Lingjie Li,Tri Dao,Zhao Song,Anshumali Shrivastava,Christopher Re | ICLR 2021,Oral | Recent advances by practitioners in the deep learning community have breathed new life into Locality Sensitive Hashing (LSH), using it to reduce memory and time bottlenecks in neural network (NN) training. However, while LSH has sub-linear guarantees for approximate near-neighbor search in theory, it is known to have inefficient query time in practice due to its use of random hash functions. Moreover, when model parameters are changing, LSH suffers from update overhead. This work is motivated by an observation that model parameters evolve slowly, such that the changes do not always require an LSH update to maintain performance. This phenomenon points to the potential for a reduction in update time and allows for a modified learnable version of data-dependent LSH to improve query time at a low cost. We use the above insights to build MONGOOSE, an end-to-end LSH framework for efficient NN training. In particular, MONGOOSE is equipped with a scheduling algorithm to adaptively perform LSH updates with provable guarantees and learnable hash functions to improve query efficiency. Empirically, we validate MONGOOSE on large-scale deep learning models for recommendation systems and language modeling. We find that it achieves up to 8% better accuracy compared to previous LSH approaches, with $6.5 \times$ speed-up and $6\times$ reduction in memory usage. | https://openreview.net/pdf/fe8456585f1f710b957e2300306b8007044772f0.pdf |
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning | https://openreview.net/forum?id=Ysuv-WOFeKR | https://openreview.net/forum?id=Ysuv-WOFeKR | Avi Singh,Huihan Liu,Gaoyue Zhou,Albert Yu,Nicholas Rhinehart,Sergey Levine | ICLR 2021,Oral | Reinforcement learning provides a general framework for flexible decision making and control, but requires extensive data collection for each new task that an agent needs to learn. In other machine learning fields, such as natural language processing or computer vision, pre-training on large, previously collected datasets to bootstrap learning for new tasks has emerged as a powerful paradigm to reduce data requirements when learning a new task. In this paper, we ask the following question: how can we enable similarly useful pre-training for RL agents? We propose a method for pre-training behavioral priors that can capture complex input-output relationships observed in successful trials from a wide range of previously seen tasks, and we show how this learned prior can be used for rapidly learning new tasks without impeding the RL agent's ability to try out novel behaviors. We demonstrate the effectiveness of our approach in challenging robotic manipulation domains involving image observations and sparse reward functions, where our method outperforms prior works by a substantial margin. Additional materials can be found on our project website: https://sites.google.com/view/parrot-rl | https://openreview.net/pdf/7ace651a0c4f40194198a9ab3ea9aefadb191c77.pdf |
SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness | https://openreview.net/forum?id=DktZb97_Fx | https://openreview.net/forum?id=DktZb97_Fx | Mikhail Yurochkin,Yuekai Sun | ICLR 2021,Oral | In this paper, we cast fair machine learning as invariant machine learning. We first formulate a version of individual fairness that enforces invariance on certain sensitive sets. We then design a transport-based regularizer that enforces this version of individual fairness and develop an algorithm to minimize the regularizer efficiently. Our theoretical results guarantee the proposed approach trains certifiably fair ML models. Finally, in the experimental studies we demonstrate improved fairness metrics in comparison to several recent fair training procedures on three ML tasks that are susceptible to algorithmic bias. | https://openreview.net/pdf/80d776638f6b356e13bef121dae894df08d5545f.pdf |
Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data | https://openreview.net/forum?id=rC8sJ4i6kaH | https://openreview.net/forum?id=rC8sJ4i6kaH | Colin Wei,Kendrick Shen,Yining Chen,Tengyu Ma | ICLR 2021,Oral | Self-training algorithms, which train a model to fit pseudolabels predicted by another previously-learned model, have been very successful for learning with unlabeled data using neural networks. However, the current theoretical understanding of self-training only applies to linear models. This work provides a unified theoretical analysis of self-training with deep networks for semi-supervised learning, unsupervised domain adaptation, and unsupervised learning. At the core of our analysis is a simple but realistic “expansion” assumption, which states that a low-probability subset of the data must expand to a neighborhood with large probability relative to the subset. We also assume that neighborhoods of examples in different classes have minimal overlap. We prove that under these assumptions, the minimizers of population objectives based on self-training and input-consistency regularization will achieve high accuracy with respect to ground-truth labels. By using off-the-shelf generalization bounds, we immediately convert this result to sample complexity guarantees for neural nets that are polynomial in the margin and Lipschitzness. Our results help explain the empirical successes of recently proposed self-training algorithms which use input consistency regularization. | https://openreview.net/pdf/fe47b1d2bc77e31125aa9437f2979edc98a6d02c.pdf |
Growing Efficient Deep Networks by Structured Continuous Sparsification | https://openreview.net/forum?id=wb3wxCObbRT | https://openreview.net/forum?id=wb3wxCObbRT | Xin Yuan,Pedro Henrique Pamplona Savarese,Michael Maire | ICLR 2021,Oral | We develop an approach to growing deep network architectures over the course of training, driven by a principled combination of accuracy and sparsity objectives. Unlike existing pruning or architecture search techniques that operate on full-sized models or supernet architectures, our method can start from a small, simple seed architecture and dynamically grow and prune both layers and filters. By combining a continuous relaxation of discrete network structure optimization with a scheme for sampling sparse subnetworks, we produce compact, pruned networks, while also drastically reducing the computational expense of training. For example, we achieve $49.7\%$ inference FLOPs and $47.4\%$ training FLOPs savings compared to a baseline ResNet-50 on ImageNet, while maintaining $75.2\%$ top-1 validation accuracy --- all without any dedicated fine-tuning stage. Experiments across CIFAR, ImageNet, PASCAL VOC, and Penn Treebank, with convolutional networks for image classification and semantic segmentation, and recurrent networks for language modeling, demonstrate that we both train faster and produce more efficient networks than competing architecture pruning or search methods. | https://openreview.net/pdf/f0340388ab26e079bb52b2e75a594fa25f418c28.pdf |
VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments | https://openreview.net/forum?id=RmB-88r9dL | https://openreview.net/forum?id=RmB-88r9dL | Lizhen Nie,Mao Ye,qiang liu,Dan Nicolae | ICLR 2021,Oral | Motivated by the rising abundance of observational data with continuous treatments, we investigate the problem of estimating the average dose-response curve (ADRF). Available parametric methods are limited in their model space, and previous attempts in leveraging neural network to enhance model expressiveness relied on partitioning continuous treatment into blocks and using separate heads for each block; this however produces in practice discontinuous ADRFs. Therefore, the question of how to adapt the structure and training of neural network to estimate ADRFs remains open. This paper makes two important contributions. First, we propose a novel varying coefficient neural network (VCNet) that improves model expressiveness while preserving continuity of the estimated ADRF. Second, to improve finite sample performance, we generalize targeted regularization to obtain a doubly robust estimator of the whole ADRF curve. | https://openreview.net/pdf/5c2b12078d2981db4bb6e855b2055a732e936fca.pdf |
EigenGame: PCA as a Nash Equilibrium | https://openreview.net/forum?id=NzTU59SYbNq | https://openreview.net/forum?id=NzTU59SYbNq | Ian Gemp,Brian McWilliams,Claire Vernade,Thore Graepel | ICLR 2021,Oral | We present a novel view on principal components analysis as a competitive game in which each approximate eigenvector is controlled by a player whose goal is to maximize their own utility function. We analyze the properties of this PCA game and the behavior of its gradient based updates. The resulting algorithm---which combines elements from Oja's rule with a generalized Gram-Schmidt orthogonalization---is naturally decentralized and hence parallelizable through message passing. We demonstrate the scalability of the algorithm with experiments on large image datasets and neural network activations. We discuss how this new view of PCA as a differentiable game can lead to further algorithmic developments and insights. | https://openreview.net/pdf/05bd1b2e95cfd532a3902be824f0945943dc7503.pdf |
Randomized Automatic Differentiation | https://openreview.net/forum?id=xpx9zj7CUlY | https://openreview.net/forum?id=xpx9zj7CUlY | Deniz Oktay,Nick McGreivy,Joshua Aduol,Alex Beatson,Ryan P Adams | ICLR 2021,Oral | The successes of deep learning, variational inference, and many other fields have been aided by specialized implementations of reverse-mode automatic differentiation (AD) to compute gradients of mega-dimensional objectives. The AD techniques underlying these tools were designed to compute exact gradients to numerical precision, but modern machine learning models are almost always trained with stochastic gradient descent. Why spend computation and memory on exact (minibatch) gradients only to use them for stochastic optimization? We develop a general framework and approach for randomized automatic differentiation (RAD), which can allow unbiased gradient estimates to be computed with reduced memory in return for variance. We examine limitations of the general approach, and argue that we must leverage problem specific structure to realize benefits. We develop RAD techniques for a variety of simple neural network architectures, and show that for a fixed memory budget, RAD converges in fewer iterations than using a small batch size for feedforward networks, and in a similar number for recurrent networks. We also show that RAD can be applied to scientific computing, and use it to develop a low-memory stochastic gradient method for optimizing the control parameters of a linear reaction-diffusion PDE representing a fission reactor. | https://openreview.net/pdf/e0f487101840a7b0e72a5ec77e3d0b6b57734740.pdf |
A Distributional Approach to Controlled Text Generation | https://openreview.net/forum?id=jWkw45-9AbL | https://openreview.net/forum?id=jWkw45-9AbL | Muhammad Khalifa,Hady Elsahar,Marc Dymetman | ICLR 2021,Oral | We propose a Distributional Approach for addressing Controlled Text Generation from pre-trained Language Models (LM). This approach permits to specify, in a single formal framework, both “pointwise’” and “distributional” constraints over the target LM — to our knowledge, the first model with such generality —while minimizing KL divergence from the initial LM distribution. The optimal target distribution is then uniquely determined as an explicit EBM (Energy-BasedModel) representation. From that optimal representation, we then train a target controlled Autoregressive LM through an adaptive distributional variant of PolicyGradient. We conduct a first set of experiments over pointwise constraints showing the advantages of our approach over a set of baselines, in terms of obtaining a controlled LM balancing constraint satisfaction with divergence from the pretrained LM. We then perform experiments over distributional constraints, a unique feature of our approach, demonstrating its potential as a remedy to the problem of Bias in Language Models. Through an ablation study, we show the effectiveness of our adaptive technique for obtaining faster convergence.
Code available at https://github.com/naver/gdc | https://openreview.net/pdf/11f0063ca9b22ee0ed8462004057c2417891ade2.pdf |
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale | https://openreview.net/forum?id=YicbFdNTTy | https://openreview.net/forum?id=YicbFdNTTy | Alexey Dosovitskiy,Lucas Beyer,Alexander Kolesnikov,Dirk Weissenborn,Xiaohua Zhai,Thomas Unterthiner,Mostafa Dehghani,Matthias Minderer,Georg Heigold,Sylvain Gelly,Jakob Uszkoreit,Neil Houlsby | ICLR 2021,Oral | While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train. | https://openreview.net/pdf/a4aa24aed16fcb6f23d1067f1a5ecf47d7115f63.pdf |
Getting a CLUE: A Method for Explaining Uncertainty Estimates | https://openreview.net/forum?id=XSLF1XFq5h | https://openreview.net/forum?id=XSLF1XFq5h | Javier Antoran,Umang Bhatt,Tameem Adel,Adrian Weller,José Miguel Hernández-Lobato | ICLR 2021,Oral | Both uncertainty estimation and interpretability are important factors for trustworthy machine learning systems. However, there is little work at the intersection of these two areas. We address this gap by proposing a novel method for interpreting uncertainty estimates from differentiable probabilistic models, like Bayesian Neural Networks (BNNs). Our method, Counterfactual Latent Uncertainty Explanations (CLUE), indicates how to change an input, while keeping it on the data manifold, such that a BNN becomes more confident about the input's prediction. We validate CLUE through 1) a novel framework for evaluating counterfactual explanations of uncertainty, 2) a series of ablation experiments, and 3) a user study. Our experiments show that CLUE outperforms baselines and enables practitioners to better understand which input patterns are responsible for predictive uncertainty. | https://openreview.net/pdf/bb1896f36e6eb8c78e3080ebea185ff4537fc95b.pdf |
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime | https://openreview.net/forum?id=PULSD5qI2N1 | https://openreview.net/forum?id=PULSD5qI2N1 | Atsushi Nitanda,Taiji Suzuki | ICLR 2021,Oral | We analyze the convergence of the averaged stochastic gradient descent for overparameterized two-layer neural networks for regression problems. It was recently found that a neural tangent kernel (NTK) plays an important role in showing the global convergence of gradient-based methods under the NTK regime, where the learning dynamics for overparameterized neural networks can be almost characterized by that for the associated reproducing kernel Hilbert space (RKHS). However, there is still room for a convergence rate analysis in the NTK regime. In this study, we show that the averaged stochastic gradient descent can achieve the minimax optimal convergence rate, with the global convergence guarantee, by exploiting the complexities of the target function and the RKHS associated with the NTK. Moreover, we show that the target function specified by the NTK of a ReLU network can be learned at the optimal convergence rate through a smooth approximation of a ReLU network under certain conditions. | https://openreview.net/pdf/70618f9539e2b69bad45f4ed3eab3fb18f352bbf.pdf |
Learning Invariant Representations for Reinforcement Learning without Reconstruction | https://openreview.net/forum?id=-2FCwDKRREu | https://openreview.net/forum?id=-2FCwDKRREu | Amy Zhang,Rowan Thomas McAllister,Roberto Calandra,Yarin Gal,Sergey Levine | ICLR 2021,Oral | We study how representation learning can accelerate reinforcement learning from rich observations, such as images, without relying either on domain knowledge or pixel-reconstruction. Our goal is to learn representations that provide for effective downstream control and invariance to task-irrelevant details. Bisimulation metrics quantify behavioral similarity between states in continuous MDPs, which we propose using to learn robust latent representations which encode only the task-relevant information from observations. Our method trains encoders such that distances in latent space equal bisimulation distances in state space. We demonstrate the effectiveness of our method at disregarding task-irrelevant information using modified visual MuJoCo tasks, where the background is replaced with moving distractors and natural videos, while achieving SOTA performance. We also test a first-person highway driving task where our method learns invariance to clouds, weather, and time of day. Finally, we provide generalization results drawn from properties of bisimulation metrics, and links to causal inference. | https://openreview.net/pdf/603fb2b2d3c728fdb6a0d1f23a60748227ff46c7.pdf |
Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability | https://openreview.net/forum?id=dYeAHXnpWJ4 | https://openreview.net/forum?id=dYeAHXnpWJ4 | Suraj Srinivas,Francois Fleuret | ICLR 2021,Oral | Current methods for the interpretability of discriminative deep neural networks commonly rely on the model's input-gradients, i.e., the gradients of the output logits w.r.t. the inputs. The common assumption is that these input-gradients contain information regarding $p_{\theta} ( y\mid \mathbf{x} )$, the model's discriminative capabilities, thus justifying their use for interpretability. However, in this work, we show that these input-gradients can be arbitrarily manipulated as a consequence of the shift-invariance of softmax without changing the discriminative function. This leaves an open question: given that input-gradients can be arbitrary, why are they highly structured and explanatory in standard models?
In this work, we re-interpret the logits of standard softmax-based classifiers as unnormalized log-densities of the data distribution and show that input-gradients can be viewed as gradients of a class-conditional generative model $p_{\theta}(\mathbf{x} \mid y)$ implicit in the discriminative model. This leads us to hypothesize that the highly structured and explanatory nature of input-gradients may be due to the alignment of this class-conditional model $p_{\theta}(\mathbf{x} \mid y)$ with that of the ground truth data distribution $p_{\text{data}} (\mathbf{x} \mid y)$. We test this hypothesis by studying the effect of density alignment on gradient explanations. To achieve this density alignment, we use an algorithm called score-matching, and propose novel approximations to this algorithm to enable training large-scale models.
Our experiments show that improving the alignment of the implicit density model with the data distribution enhances gradient structure and explanatory power while reducing this alignment has the opposite effect. This also leads us to conjecture that unintended density alignment in standard neural network training may explain the highly structured nature of input-gradients observed in practice. Overall, our finding that input-gradients capture information regarding an implicit generative model implies that we need to re-think their use for interpreting discriminative models. | https://openreview.net/pdf/b29e31cf78e011a59f9e49950670211d4c516b00.pdf |
SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments | https://openreview.net/forum?id=cPZOyoDloxl | https://openreview.net/forum?id=cPZOyoDloxl | Glen Berseth,Daniel Geng,Coline Manon Devin,Nicholas Rhinehart,Chelsea Finn,Dinesh Jayaraman,Sergey Levine | ICLR 2021,Oral | Every living organism struggles against disruptive environmental forces to carve out and maintain an orderly niche. We propose that such a struggle to achieve and preserve order might offer a principle for the emergence of useful behaviors in artificial agents. We formalize this idea into an unsupervised reinforcement learning method called surprise minimizing reinforcement learning (SMiRL). SMiRL alternates between learning a density model to evaluate the surprise of a stimulus, and improving the policy to seek more predictable stimuli. The policy seeks out stable and repeatable situations that counteract the environment's prevailing sources of entropy. This might include avoiding other hostile agents, or finding a stable, balanced pose for a bipedal robot in the face of disturbance forces. We demonstrate that our surprise minimizing agents can successfully play Tetris, Doom, control a humanoid to avoid falls, and navigate to escape enemies in a maze without any task-specific reward supervision. We further show that SMiRL can be used together with standard task rewards to accelerate reward-driven learning. | https://openreview.net/pdf/f9be390d21352320ef34c706df775ded52b79ebd.pdf |
Learning to Reach Goals via Iterated Supervised Learning | https://openreview.net/forum?id=rALA0Xo6yNJ | https://openreview.net/forum?id=rALA0Xo6yNJ | Dibya Ghosh,Abhishek Gupta,Ashwin Reddy,Justin Fu,Coline Manon Devin,Benjamin Eysenbach,Sergey Levine | ICLR 2021,Oral | Current reinforcement learning (RL) algorithms can be brittle and difficult to use, especially when learning goal-reaching behaviors from sparse rewards. Although supervised imitation learning provides a simple and stable alternative, it requires access to demonstrations from a human supervisor. In this paper, we study RL algorithms that use imitation learning to acquire goal reaching policies from scratch, without the need for expert demonstrations or a value function. In lieu of demonstrations, we leverage the property that any trajectory is a successful demonstration for reaching the final state in that same trajectory. We propose a simple algorithm in which an agent continually relabels and imitates the trajectories it generates to progressively learn goal-reaching behaviors from scratch. Each iteration, the agent collects new trajectories using the latest policy, and maximizes the likelihood of the actions along these trajectories under the goal that was actually reached, so as to improve the policy. We formally show that this iterated supervised learning procedure optimizes a bound on the RL objective, derive performance bounds of the learned policy, and empirically demonstrate improved goal-reaching performance and robustness over current RL algorithms in several benchmark tasks. | https://openreview.net/pdf/34b6d953408a7aaff3549569738b80162e8e6dbc.pdf |
Self-training For Few-shot Transfer Across Extreme Task Differences | https://openreview.net/forum?id=O3Y56aqpChA | https://openreview.net/forum?id=O3Y56aqpChA | Cheng Perng Phoo,Bharath Hariharan | ICLR 2021,Oral | Most few-shot learning techniques are pre-trained on a large, labeled “base dataset”. In problem domains where such large labeled datasets are not available for pre-training (e.g., X-ray, satellite images), one must resort to pre-training in a different “source” problem domain (e.g., ImageNet), which can be very different from the desired target task. Traditional few-shot and transfer learning techniques fail in the presence of such extreme differences between the source and target tasks. In this paper, we present a simple and effective solution to tackle this extreme domain gap: self-training a source domain representation on unlabeled data from the target domain. We show that this improves one-shot performance on the target domain by 2.9 points on average on the challenging BSCD-FSL benchmark consisting of datasets from multiple domains. | https://openreview.net/pdf/1b36075cafa13e37ec6b9a3796f921d4d587ac0a.pdf |
Federated Learning Based on Dynamic Regularization | https://openreview.net/forum?id=B7v4QMR6Z9w | https://openreview.net/forum?id=B7v4QMR6Z9w | Durmus Alp Emre Acar,Yue Zhao,Ramon Matas,Matthew Mattina,Paul Whatmough,Venkatesh Saligrama | ICLR 2021,Oral | We propose a novel federated learning method for distributively training neural network models, where the server orchestrates cooperation between a subset of randomly chosen devices in each round. We view Federated Learning problem primarily from a communication perspective and allow more device level computations to save transmission costs. We point out a fundamental dilemma, in that the minima of the local-device level empirical loss are inconsistent with those of the global empirical loss. Different from recent prior works, that either attempt inexact minimization or utilize devices for parallelizing gradient computation, we propose a dynamic regularizer for each device at each round, so that in the limit the global and device solutions are aligned. We demonstrate both through empirical results on real and synthetic data as well as analytical results that our scheme leads to efficient training, in both convex and non-convex settings, while being fully agnostic to device heterogeneity and robust to large number of devices, partial participation and unbalanced data. | https://openreview.net/pdf/d5d8224518e951c4f83d7ee7338ee5862ea09a04.pdf |
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator | https://openreview.net/forum?id=Mk6PZtgAgfq | https://openreview.net/forum?id=Mk6PZtgAgfq | Max B Paulus,Chris J. Maddison,Andreas Krause | ICLR 2021,Oral | Gradient estimation in models with discrete latent variables is a challenging problem, because the simplest unbiased estimators tend to have high variance. To counteract this, modern estimators either introduce bias, rely on multiple function evaluations, or use learned, input-dependent baselines. Thus, there is a need for estimators that require minimal tuning, are computationally cheap, and have low mean squared error. In this paper, we show that the variance of the straight-through variant of the popular Gumbel-Softmax estimator can be reduced through Rao-Blackwellization without increasing the number of function evaluations. This provably reduces the mean squared error. We empirically demonstrate that this leads to variance reduction, faster convergence, and generally improved performance in two unsupervised latent variable models. | https://openreview.net/pdf/99dded8f5b416c9596f112afe789c337af877339.pdf |
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies | https://openreview.net/forum?id=F3s69XzWOia | https://openreview.net/forum?id=F3s69XzWOia | T. Konstantin Rusch,Siddhartha Mishra | ICLR 2021,Oral | Circuits of biological neurons, such as in the functional parts of the brain can be modeled as networks of coupled oscillators. Inspired by the ability of these systems to express a rich set of outputs while keeping (gradients of) state variables bounded, we propose a novel architecture for recurrent neural networks. Our proposed RNN is based on a time-discretization of a system of second-order ordinary differential equations, modeling networks of controlled nonlinear oscillators. We prove precise bounds on the gradients of the hidden states, leading to the mitigation of the exploding and vanishing gradient problem for this RNN. Experiments show that the proposed RNN is comparable in performance to the state of the art on a variety of benchmarks, demonstrating the potential of this architecture to provide stable and accurate RNNs for processing complex sequential data. | https://openreview.net/pdf/e69f77961c03aeefb3d2dfcc2c77185ac11781db.pdf |
DiffWave: A Versatile Diffusion Model for Audio Synthesis | https://openreview.net/forum?id=a-xFK8Ymz5J | https://openreview.net/forum?id=a-xFK8Ymz5J | Zhifeng Kong,Wei Ping,Jiaji Huang,Kexin Zhao,Bryan Catanzaro | ICLR 2021,Oral | In this work, we propose DiffWave, a versatile diffusion probabilistic model for conditional and unconditional waveform generation. The model is non-autoregressive, and converts the white noise signal into structured waveform through a Markov chain with a constant number of steps at synthesis. It is efficiently trained by optimizing a variant of variational bound on the data likelihood. DiffWave produces high-fidelity audios in different waveform generation tasks, including neural vocoding conditioned on mel spectrogram, class-conditional generation, and unconditional generation. We demonstrate that DiffWave matches a strong WaveNet vocoder in terms of speech quality (MOS: 4.44 versus 4.43), while synthesizing orders of magnitude faster. In particular, it significantly outperforms autoregressive and GAN-based waveform models in the challenging unconditional generation task in terms of audio quality and sample diversity from various automatic and human evaluations. | https://openreview.net/pdf/d27840fc3a835c4da4a9d13c4227c7a0d8a9b3c5.pdf |
Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistency | https://openreview.net/forum?id=QIRlze3I6hX | https://openreview.net/forum?id=QIRlze3I6hX | Qiang Zhang,Tete Xiao,Alexei A Efros,Lerrel Pinto,Xiaolong Wang | ICLR 2021,Oral | At the heart of many robotics problems is the challenge of learning correspondences across domains. For instance, imitation learning requires obtaining correspondence between humans and robots; sim-to-real requires correspondence between physics simulators and real hardware; transfer learning requires correspondences between different robot environments. In this paper, we propose to learn correspondence across such domains emphasizing on differing modalities (vision and internal state), physics parameters (mass and friction), and morphologies (number of limbs). Importantly, correspondences are learned using unpaired and randomly collected data from the two domains. We propose dynamics cycles that align dynamic robotic behavior across two domains using a cycle consistency constraint. Once this correspondence is found, we can directly transfer the policy trained on one domain to the other, without needing any additional fine-tuning on the second domain. We perform experiments across a variety of problem domains, both in simulation and on real robots. Our framework is able to align uncalibrated monocular video of a real robot arm to dynamic state-action trajectories of a simulated arm without paired data. Video demonstrations of our results are available at: https://sites.google.com/view/cycledynamics . | https://openreview.net/pdf/2d895dd1e0137048768cefacc5fe9d81daa35d58.pdf |
Deformable DETR: Deformable Transformers for End-to-End Object Detection | https://openreview.net/forum?id=gZ9hCDWe6ke | https://openreview.net/forum?id=gZ9hCDWe6ke | Xizhou Zhu,Weijie Su,Lewei Lu,Bin Li,Xiaogang Wang,Jifeng Dai | ICLR 2021,Oral | DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, due to the limitation of Transformer attention modules in processing image feature maps. To mitigate these issues, we proposed Deformable DETR, whose attention modules only attend to a small set of key sampling points around a reference. Deformable DETR can achieve better performance than DETR (especially on small objects) with 10$\times$ less training epochs. Extensive experiments on the COCO benchmark demonstrate the effectiveness of our approach. Code is released at https://github.com/fundamentalvision/Deformable-DETR. | https://openreview.net/pdf/758d4b5c0d63033d526ff8744d872a03543bb674.pdf |
Learning Generalizable Visual Representations via Interactive Gameplay | https://openreview.net/forum?id=UuchYL8wSZo | https://openreview.net/forum?id=UuchYL8wSZo | Luca Weihs,Aniruddha Kembhavi,Kiana Ehsani,Sarah M Pratt,Winson Han,Alvaro Herrasti,Eric Kolve,Dustin Schwenk,Roozbeh Mottaghi,Ali Farhadi | ICLR 2021,Oral | A growing body of research suggests that embodied gameplay, prevalent not just in human cultures but across a variety of animal species including turtles and ravens, is critical in developing the neural flexibility for creative problem solving, decision making, and socialization. Comparatively little is known regarding the impact of embodied gameplay upon artificial agents. While recent work has produced agents proficient in abstract games, these environments are far removed the real world and thus these agents can provide little insight into the advantages of embodied play. Hiding games, such as hide-and-seek, played universally, provide a rich ground for studying the impact of embodied gameplay on representation learning in the context of perspective taking, secret keeping, and false belief understanding. Here we are the first to show that embodied adversarial reinforcement learning agents playing Cache, a variant of hide-and-seek, in a high fidelity, interactive, environment, learn generalizable representations of their observations encoding information such as object permanence, free space, and containment. Moving closer to biologically motivated learning strategies, our agents' representations, enhanced by intentionality and memory, are developed through interaction and play. These results serve as a model for studying how facets of vision develop through interaction, provide an experimental framework for assessing what is learned by artificial agents, and demonstrates the value of moving from large, static, datasets towards experiential, interactive, representation learning. | https://openreview.net/pdf/9136e8ff7bc8b8e82f73830b816f60fe258ec628.pdf |
Gradient Projection Memory for Continual Learning | https://openreview.net/forum?id=3AOj0RCNC2 | https://openreview.net/forum?id=3AOj0RCNC2 | Gobinda Saha,Isha Garg,Kaushik Roy | ICLR 2021,Oral | The ability to learn continually without forgetting the past tasks is a desired attribute for artificial learning systems. Existing approaches to enable such learning in artificial neural networks usually rely on network growth, importance based weight update or replay of old data from the memory. In contrast, we propose a novel approach where a neural network learns new tasks by taking gradient steps in the orthogonal direction to the gradient subspaces deemed important for the past tasks. We find the bases of these subspaces by analyzing network representations (activations) after learning each task with Singular Value Decomposition (SVD) in a single shot manner and store them in the memory as Gradient Projection Memory (GPM). With qualitative and quantitative analyses, we show that such orthogonal gradient descent induces minimum to no interference with the past tasks, thereby mitigates forgetting. We evaluate our algorithm on diverse image classification datasets with short and long sequences of tasks and report better or on-par performance compared to the state-of-the-art approaches. | https://openreview.net/pdf/a65e5f689852fad25dc117881988232e0f95ed52.pdf |
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting | https://openreview.net/forum?id=kmG8vRXTFv | https://openreview.net/forum?id=kmG8vRXTFv | Yuan Yin,Vincent LE GUEN,Jérémie DONA,Emmanuel de Bezenac,Ibrahim Ayed,Nicolas THOME,patrick gallinari | ICLR 2021,Oral | Forecasting complex dynamical phenomena in settings where only partial knowledge of their dynamics is available is a prevalent problem across various scientific fields. While purely data-driven approaches are arguably insufficient in this context, standard physical modeling based approaches tend to be over-simplistic, inducing non-negligible errors. In this work, we introduce the APHYNITY framework, a principled approach for augmenting incomplete physical dynamics described by differential equations with deep data-driven models. It consists in decomposing the dynamics into two components: a physical component accounting for the dynamics for which we have some prior knowledge, and a data-driven component accounting for errors of the physical model. The learning problem is carefully formulated such that the physical model explains as much of the data as possible, while the data-driven component only describes information that cannot be captured by the physical model, no more, no less. This not only provides the existence and uniqueness for this decomposition, but also ensures interpretability and benefits generalization. Experiments made on three important use cases, each representative of a different family of phenomena, i.e. reaction-diffusion equations, wave equations and the non-linear damped pendulum, show that APHYNITY can efficiently leverage approximate physical models to accurately forecast the evolution of the system and correctly identify relevant physical parameters. | https://openreview.net/pdf/26f8f5665ae13d683ef87f2ff8c2e5a321fac386.pdf |
Human-Level Performance in No-Press Diplomacy via Equilibrium Search | https://openreview.net/forum?id=0-uUGPbIjD | https://openreview.net/forum?id=0-uUGPbIjD | Jonathan Gray,Adam Lerer,Anton Bakhtin,Noam Brown | ICLR 2021,Oral | Prior AI breakthroughs in complex games have focused on either the purely adversarial or purely cooperative settings. In contrast, Diplomacy is a game of shifting alliances that involves both cooperation and competition. For this reason, Diplomacy has proven to be a formidable research challenge. In this paper we describe an agent for the no-press variant of Diplomacy that combines supervised learning on human data with one-step lookahead search via regret minimization. Regret minimization techniques have been behind previous AI successes in adversarial games, most notably poker, but have not previously been shown to be successful in large-scale games involving cooperation. We show that our agent greatly exceeds the performance of past no-press Diplomacy bots, is unexploitable by expert humans, and ranks in the top 2% of human players when playing anonymous games on a popular Diplomacy website. | https://openreview.net/pdf/59e1f6ceb25265194013bc67945a7001ef36ca84.pdf |
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks | https://openreview.net/forum?id=UH-cmocLJC | https://openreview.net/forum?id=UH-cmocLJC | Keyulu Xu,Mozhi Zhang,Jingling Li,Simon Shaolei Du,Ken-Ichi Kawarabayashi,Stefanie Jegelka | ICLR 2021,Oral | We study how neural networks trained by gradient descent extrapolate, i.e., what they learn outside the support of the training distribution. Previous works report mixed empirical results when extrapolating with neural networks: while feedforward neural networks, a.k.a. multilayer perceptrons (MLPs), do not extrapolate well in certain simple tasks, Graph Neural Networks (GNNs) -- structured networks with MLP modules -- have shown some success in more complex tasks. Working towards a theoretical explanation, we identify conditions under which MLPs and GNNs extrapolate well. First, we quantify the observation that ReLU MLPs quickly converge to linear functions along any direction from the origin, which implies that ReLU MLPs do not extrapolate most nonlinear functions. But, they can provably learn a linear target function when the training distribution is sufficiently diverse. Second, in connection to analyzing the successes and limitations of GNNs, these results suggest a hypothesis for which we provide theoretical and empirical evidence: the success of GNNs in extrapolating algorithmic tasks to new data (e.g., larger graphs or edge weights) relies on encoding task-specific non-linearities in the architecture or features. Our theoretical analysis builds on a connection of over-parameterized networks to the neural tangent kernel. Empirically, our theory holds across different training settings. | https://openreview.net/pdf/eb776544f1a0835881289e4dad2436f38f88269d.pdf |
Rethinking Attention with Performers | https://openreview.net/forum?id=Ua6zuk0WRH | https://openreview.net/forum?id=Ua6zuk0WRH | Krzysztof Marcin Choromanski,Valerii Likhosherstov,David Dohan,Xingyou Song,Andreea Gane,Tamas Sarlos,Peter Hawkins,Jared Quincy Davis,Afroz Mohiuddin,Lukasz Kaiser,David Benjamin Belanger,Lucy J Colwell,Adrian Weller | ICLR 2021,Oral | We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors such as sparsity or low-rankness. To approximate softmax attention-kernels, Performers use a novel Fast Attention Via positive Orthogonal Random features approach (FAVOR+), which may be of independent interest for scalable kernel methods. FAVOR+ can also be used to efficiently model kernelizable attention mechanisms beyond softmax. This representational power is crucial to accurately compare softmax with other kernels for the first time on large-scale tasks, beyond the reach of regular Transformers, and investigate optimal attention-kernels. Performers are linear architectures fully compatible with regular Transformers and with strong theoretical guarantees: unbiased or nearly-unbiased estimation of the attention matrix, uniform convergence and low estimation variance. We tested Performers on a rich set of tasks stretching from pixel-prediction through text models to protein sequence modeling. We demonstrate competitive results with other examined efficient sparse and dense attention methods, showcasing effectiveness of the novel attention-learning paradigm leveraged by Performers. | https://openreview.net/pdf/f9985b6b0f77c997ffb932a86a3f3ff482aaa30d.pdf |
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study | https://openreview.net/forum?id=nIAxjsniDzg | https://openreview.net/forum?id=nIAxjsniDzg | Marcin Andrychowicz,Anton Raichuk,Piotr Stańczyk,Manu Orsini,Sertan Girgin,Raphaël Marinier,Leonard Hussenot,Matthieu Geist,Olivier Pietquin,Marcin Michalski,Sylvain Gelly,Olivier Bachem | ICLR 2021,Oral | In recent years, reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple, their state-of-the-art implementations take numerous low- and high-level design decisions that strongly affect the performance of the resulting agents. Those choices are usually not extensively discussed in the literature, leading to discrepancy between published descriptions of algorithms and their implementations. This makes it hard to attribute progress in RL and slows down overall progress [Engstrom'20]. As a step towards filling that gap, we implement >50 such ``"choices" in a unified on-policy deep actor-critic framework, allowing us to investigate their impact in a large-scale empirical study. We train over 250'000 agents in five continuous control environments of different complexity and provide insights and practical recommendations for the training of on-policy deep actor-critic RL agents. | https://openreview.net/pdf/6e07c0b9828c97445a9667bf90fe254fa7de8e3a.pdf |
Neural Synthesis of Binaural Speech From Mono Audio | https://openreview.net/forum?id=uAX8q61EVRu | https://openreview.net/forum?id=uAX8q61EVRu | Alexander Richard,Dejan Markovic,Israel D. Gebru,Steven Krenn,Gladstone Alexander Butler,Fernando Torre,Yaser Sheikh | ICLR 2021,Oral | We present a neural rendering approach for binaural sound synthesis that can produce realistic and spatially accurate binaural sound in realtime. The network takes, as input, a single-channel audio source and synthesizes, as output, two-channel binaural sound, conditioned on the relative position and orientation of the listener with respect to the source. We investigate deficiencies of the l2-loss on raw waveforms in a theoretical analysis and introduce an improved loss that overcomes these limitations. In an empirical evaluation, we establish that our approach is the first to generate spatially accurate waveform outputs (as measured by real recordings) and outperforms existing approaches by a considerable margin, both quantitatively and in a perceptual study. Dataset and code are available online. | https://openreview.net/pdf/1d9f3bd5393a688e9b4ad20e8f71e32c0ff119e8.pdf |
Dataset Condensation with Gradient Matching | https://openreview.net/forum?id=mSAKhLYLSsl | https://openreview.net/forum?id=mSAKhLYLSsl | Bo Zhao,Konda Reddy Mopuri,Hakan Bilen | ICLR 2021,Oral | As the state-of-the-art machine learning methods in many fields rely on larger datasets, storing datasets and training models on them become significantly more expensive. This paper proposes a training set synthesis technique for data-efficient learning, called Dataset Condensation, that learns to condense large dataset into a small set of informative synthetic samples for training deep neural networks from scratch. We formulate this goal as a gradient matching problem between the gradients of deep neural network weights that are trained on the original and our synthetic data. We rigorously evaluate its performance in several computer vision benchmarks and demonstrate that it significantly outperforms the state-of-the-art methods. Finally we explore the use of our method in continual learning and neural architecture search and report promising gains when limited memory and computations are available. | https://openreview.net/pdf/f6babe705b5d43a6a88ee3b553c209341b63a5e0.pdf |
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes | https://openreview.net/forum?id=HajQFbx_yB | https://openreview.net/forum?id=HajQFbx_yB | Mike Gartrell,Insu Han,Elvis Dohmatob,Jennifer Gillenwater,Victor-Emmanuel Brunel | ICLR 2021,Oral | Determinantal point processes (DPPs) have attracted significant attention in machine learning for their ability to model subsets drawn from a large item collection. Recent work shows that nonsymmetric DPP (NDPP) kernels have significant advantages over symmetric kernels in terms of modeling power and predictive performance. However, for an item collection of size $M$, existing NDPP learning and inference algorithms require memory quadratic in $M$ and runtime cubic (for learning) or quadratic (for inference) in $M$, making them impractical for many typical subset selection tasks. In this work, we develop a learning algorithm with space and time requirements linear in $M$ by introducing a new NDPP kernel decomposition. We also derive a linear-complexity NDPP maximum a posteriori (MAP) inference algorithm that applies not only to our new kernel but also to that of prior work. Through evaluation on real-world datasets, we show that our algorithms scale significantly better, and can match the predictive performance of prior work. | https://openreview.net/pdf/8986ce3acb4c64c6f02ae25ebddca92808aac43c.pdf |
Geometry-aware Instance-reweighted Adversarial Training | https://openreview.net/forum?id=iAX0l6Cz8ub | https://openreview.net/forum?id=iAX0l6Cz8ub | Jingfeng Zhang,Jianing Zhu,Gang Niu,Bo Han,Masashi Sugiyama,Mohan Kankanhalli | ICLR 2021,Oral | In adversarial machine learning, there was a common belief that robustness and accuracy hurt each other. The belief was challenged by recent studies where we can maintain the robustness and improve the accuracy. However, the other direction, whether we can keep the accuracy and improve the robustness, is conceptually and practically more interesting, since robust accuracy should be lower than standard accuracy for any model. In this paper, we show this direction is also promising. Firstly, we find even over-parameterized deep networks may still have insufficient model capacity, because adversarial training has an overwhelming smoothing effect. Secondly, given limited model capacity, we argue adversarial data should have unequal importance: geometrically speaking, a natural data point closer to/farther from the class boundary is less/more robust, and the corresponding adversarial data point should be assigned with larger/smaller weight. Finally, to implement the idea, we propose geometry-aware instance-reweighted adversarial training, where the weights are based on how difficult it is to attack a natural data point. Experiments show that our proposal boosts the robustness of standard adversarial training; combining two directions, we improve both robustness and accuracy of standard adversarial training. | https://openreview.net/pdf/746bc7119712b56c0426659dcb575f5864fa29ec.pdf |
Free Lunch for Few-shot Learning: Distribution Calibration | https://openreview.net/forum?id=JWOiYxMG92s | https://openreview.net/forum?id=JWOiYxMG92s | Shuo Yang,Lu Liu,Min Xu | ICLR 2021,Oral | Learning from a limited number of samples is challenging since the learned model can easily become overfitted based on the biased distribution formed by only a few training examples. In this paper, we calibrate the distribution of these few-sample classes by transferring statistics from the classes with sufficient examples. Then an adequate number of examples can be sampled from the calibrated distribution to expand the inputs to the classifier. We assume every dimension in the feature representation follows a Gaussian distribution so that the mean and the variance of the distribution can borrow from that of similar classes whose statistics are better estimated with an adequate number of samples. Our method can be built on top of off-the-shelf pretrained feature extractors and classification models without extra parameters. We show that a simple logistic regression classifier trained using the features sampled from our calibrated distribution can outperform the state-of-the-art accuracy on three datasets (~5% improvement on miniImageNet compared to the next best). The visualization of these generated features demonstrates that our calibrated distribution is an accurate estimation. | https://openreview.net/pdf/657c8cfa6bb8f5a10bf00d0e87e08b924e17fcf6.pdf |
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding | https://openreview.net/forum?id=EbIDjBynYJ8 | https://openreview.net/forum?id=EbIDjBynYJ8 | David A. Klindt,Lukas Schott,Yash Sharma,Ivan Ustyuzhaninov,Wieland Brendel,Matthias Bethge,Dylan Paiton | ICLR 2021,Oral | Disentangling the underlying generative factors from complex data has so far been limited to carefully constructed scenarios. We propose a path towards natural data by first showing that the statistics of natural data provide enough structure to enable disentanglement, both theoretically and empirically. Specifically, we provide evidence that objects in natural movies undergo transitions that are typically small in magnitude with occasional large jumps, which is characteristic of a temporally sparse distribution. To address this finding we provide a novel proof that relies on a sparse prior on temporally adjacent observations to recover the true latent variables up to permutations and sign flips, directly providing a stronger result than previous work. We show that equipping practical estimation methods with our prior often surpasses the current state-of-the-art on several established benchmark datasets without any impractical assumptions, such as knowledge of the number of changing generative factors. Furthermore, we contribute two new benchmarks, Natural Sprites and KITTI Masks, which integrate the measured natural dynamics to enable disentanglement evaluation with more realistic datasets. We leverage these benchmarks to test our theory, demonstrating improved performance. We also identify non-obvious challenges for current methods in scaling to more natural domains. Taken together our work addresses key issues in disentanglement research for moving towards more natural settings. | https://openreview.net/pdf/70b2f88dcd46ac3a433a562c71a9d82d8dffb042.pdf |
Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs | https://openreview.net/forum?id=FGqiDsBUKL0 | https://openreview.net/forum?id=FGqiDsBUKL0 | Xingang Pan,Bo Dai,Ziwei Liu,Chen Change Loy,Ping Luo | ICLR 2021,Oral | Natural images are projections of 3D objects on a 2D image plane. While state-of-the-art 2D generative models like GANs show unprecedented quality in modeling the natural image manifold, it is unclear whether they implicitly capture the underlying 3D object structures. And if so, how could we exploit such knowledge to recover the 3D shapes of objects in the images? To answer these questions, in this work, we present the first attempt to directly mine 3D geometric cues from an off-the-shelf 2D GAN that is trained on RGB images only. Through our investigation, we found that such a pre-trained GAN indeed contains rich 3D knowledge and thus can be used to recover 3D shape from a single 2D image in an unsupervised manner. The core of our framework is an iterative strategy that explores and exploits diverse viewpoint and lighting variations in the GAN image manifold. The framework does not require 2D keypoint or 3D annotations, or strong assumptions on object shapes (e.g. shapes are symmetric), yet it successfully recovers 3D shapes with high precision for human faces, cats, cars, and buildings. The recovered 3D shapes immediately allow high-quality image editing like relighting and object rotation. We quantitatively demonstrate the effectiveness of our approach compared to previous methods in both 3D shape reconstruction and face rotation. Our code is available at https://github.com/XingangPan/GAN2Shape. | https://openreview.net/pdf/ced9a36d7c1942426cc47913c3ec31367b60dad3.pdf |
Net-DNF: Effective Deep Modeling of Tabular Data | https://openreview.net/forum?id=73WTGs96kho | https://openreview.net/forum?id=73WTGs96kho | Liran Katzir,Gal Elidan,Ran El-Yaniv | ICLR 2021,Poster | A challenging open question in deep learning is how to handle tabular data. Unlike domains such as image and natural language processing, where deep architectures prevail, there is still no widely accepted neural architecture that dominates tabular data. As a step toward bridging this gap, we present Net-DNF a novel generic architecture whose inductive bias elicits models whose structure corresponds to logical Boolean formulas in disjunctive normal form (DNF) over affine soft-threshold decision terms. Net-DNFs also promote localized decisions that are taken over small subsets of the features. We present an extensive experiments showing that Net-DNFs significantly and consistently outperform fully connected networks over tabular data. With relatively few hyperparameters, Net-DNFs open the door to practical end-to-end handling of tabular data using neural networks. We present ablation studies, which justify the design choices of Net-DNF including the inductive bias elements, namely, Boolean formulation, locality, and feature selection.
| https://openreview.net/pdf/0faa3d246b873caa0ff0a42200c685558260733c.pdf |
Predicting Inductive Biases of Pre-Trained Models | https://openreview.net/forum?id=mNtmhaDkAr | https://openreview.net/forum?id=mNtmhaDkAr | Charles Lovering,Rohan Jha,Tal Linzen,Ellie Pavlick | ICLR 2021,Poster | Most current NLP systems are based on a pre-train-then-fine-tune paradigm, in which a large neural network is first trained in a self-supervised way designed to encourage the network to extract broadly-useful linguistic features, and then fine-tuned for a specific task of interest. Recent work attempts to understand why this recipe works and explain when it fails. Currently, such analyses have produced two sets of apparently-contradictory results. Work that analyzes the representations that result from pre-training (via "probing classifiers") finds evidence that rich features of linguistic structure can be decoded with high accuracy, but work that analyzes model behavior after fine-tuning (via "challenge sets") indicates that decisions are often not based on such structure but rather on spurious heuristics specific to the training set. In this work, we test the hypothesis that the extent to which a feature influences a model's decisions can be predicted using a combination of two factors: The feature's "extractability" after pre-training (measured using information-theoretic probing techniques), and the "evidence" available during fine-tuning (defined as the feature's co-occurrence rate with the label). In experiments with both synthetic and natural language data, we find strong evidence (statistically significant correlations) supporting this hypothesis. | https://openreview.net/pdf/5f8e7508b216ea50a36e7f4584e4e6d8953917be.pdf |
Optimism in Reinforcement Learning with Generalized Linear Function Approximation | https://openreview.net/forum?id=CBmJwzneppz | https://openreview.net/forum?id=CBmJwzneppz | Yining Wang,Ruosong Wang,Simon Shaolei Du,Akshay Krishnamurthy | ICLR 2021,Poster | We design a new provably efficient algorithm for episodic reinforcement learning with generalized linear function approximation. We analyze the algorithm under a new expressivity assumption that we call ``optimistic closure,'' which is strictly weaker than assumptions from prior analyses for the linear setting. With optimistic closure, we prove that our algorithm enjoys a regret bound of $\widetilde{O}\left(H\sqrt{d^3 T}\right)$ where $H$ is the horizon, $d$ is the dimensionality of the state-action features and $T$ is the number of episodes. This is the first statistically and computationally efficient algorithm for reinforcement learning with generalized linear functions. | https://openreview.net/pdf/a233880f2576e4e435aefcdb4dce873e39557072.pdf |
SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing | https://openreview.net/forum?id=oyZxhRI2RiE | https://openreview.net/forum?id=oyZxhRI2RiE | Tao Yu,Rui Zhang,Alex Polozov,Christopher Meek,Ahmed Hassan Awadallah | ICLR 2021,Poster | Conversational Semantic Parsing (CSP) is the task of converting a sequence of natural language queries to formal language (e.g., SQL, SPARQL) that can be executed against a structured ontology (e.g. databases, knowledge bases). To accomplish this task, a CSP system needs to model the relation between the unstructured language utterance and the structured ontology while representing the multi-turn dynamics of the dialog. Pre-trained language models (LMs) are the state-of-the-art for various natural language processing tasks. However, existing pre-trained LMs that use language modeling training objectives over free-form text have limited ability to represent natural language references to contextual structural data. In this work, we present SCORE, a new pre-training approach for CSP tasks designed to induce representations that capture the alignment between the dialogue flow and the structural context. We demonstrate the broad applicability of SCORE to CSP tasks by combining SCORE with strong base systems on four different tasks (SPARC, COSQL, MWOZ, and SQA). We show that SCORE can improve the performance over all these base systems by a significant margin and achieves state-of-the-art results on three of them. | https://openreview.net/pdf/5f0fb4ec7db7adfa1bbec5ca4828380eed47533f.pdf |
A teacher-student framework to distill future trajectories | https://openreview.net/forum?id=ECuvULjFQia | https://openreview.net/forum?id=ECuvULjFQia | Alexander Neitz,Giambattista Parascandolo,Bernhard Schölkopf | ICLR 2021,Poster | By learning to predict trajectories of dynamical systems, model-based methods can make extensive use of all observations from past experience. However, due to partial observability, stochasticity, compounding errors, and irrelevant dynamics, training to predict observations explicitly often results in poor models. Model-free techniques try to side-step the problem by learning to predict values directly. While breaking the explicit dependency on future observations can result in strong performance, this usually comes at the cost of low sample efficiency, as the abundant information about the dynamics contained in future observations goes unused. Here we take a step back from both approaches: Instead of hand-designing how trajectories should be incorporated, a teacher network learns to interpret the trajectories and to provide target activations which guide a student model that can only observe the present. The teacher is trained with meta-gradients to maximize the student's performance on a validation set. We show that our approach performs well on tasks that are difficult for model-free and model-based methods, and we study the role of every component through ablation studies. | https://openreview.net/pdf/92e8f1e3d126b2094e7c7fd20bede53365fc6ea9.pdf |
Certify or Predict: Boosting Certified Robustness with Compositional Architectures | https://openreview.net/forum?id=USCNapootw | https://openreview.net/forum?id=USCNapootw | Mark Niklas Mueller,Mislav Balunovic,Martin Vechev | ICLR 2021,Poster | A core challenge with existing certified defense mechanisms is that while they improve certified robustness, they also tend to drastically decrease natural accuracy, making it difficult to use these methods in practice. In this work, we propose a new architecture which addresses this challenge and enables one to boost the certified robustness of any state-of-the-art deep network, while controlling the overall accuracy loss, without requiring retraining. The key idea is to combine this model with a (smaller) certified network where at inference time, an adaptive selection mechanism decides on the network to process the input sample. The approach is compositional: one can combine any pair of state-of-the-art (e.g., EfficientNet or ResNet) and certified networks, without restriction. The resulting architecture enables much higher natural accuracy than previously possible with certified defenses alone, while substantially boosting the certified robustness of deep networks. We demonstrate the effectiveness of this adaptive approach on a variety of datasets and architectures. For instance, on CIFAR-10 with an $\ell_\infty$ perturbation of 2/255, we are the first to obtain a high natural accuracy (90.1%) with non-trivial certified robustness (27.5%). Notably, prior state-of-the-art methods incur a substantial drop in accuracy for a similar certified robustness. | https://openreview.net/pdf/9e6eb18330c25797cf3af7f9a23a4ac3a44de56c.pdf |
On the Transfer of Disentangled Representations in Realistic Settings | https://openreview.net/forum?id=8VXvj1QNRl1 | https://openreview.net/forum?id=8VXvj1QNRl1 | Andrea Dittadi,Frederik Träuble,Francesco Locatello,Manuel Wuthrich,Vaibhav Agrawal,Ole Winther,Stefan Bauer,Bernhard Schölkopf | ICLR 2021,Poster | Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning. While disentangled representations were found to be useful for diverse tasks such as abstract reasoning and fair classification, their scalability and real-world impact remain questionable.
We introduce a new high-resolution dataset with 1M simulated images and over 1,800 annotated real-world images of the same setup. In contrast to previous work, this new dataset exhibits correlations, a complex underlying structure, and allows to evaluate transfer to unseen simulated and real-world settings where the encoder i) remains in distribution or ii) is out of distribution.
We propose new architectures in order to scale disentangled representation learning to realistic high-resolution settings and conduct a large-scale empirical study of disentangled representations on this dataset. We observe that disentanglement is a good predictor for out-of-distribution (OOD) task performance. | https://openreview.net/pdf/bd4ae699aba426a89c027ef66f9f0cc2dcb0e187.pdf |
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary | https://openreview.net/forum?id=sCZbhBvqQaU | https://openreview.net/forum?id=sCZbhBvqQaU | Huan Zhang,Hongge Chen,Duane S Boning,Cho-Jui Hsieh | ICLR 2021,Poster | We study the robustness of reinforcement learning (RL) with adversarially perturbed state observations, which aligns with the setting of many adversarial attacks to deep reinforcement learning (DRL) and is also important for rolling out real-world RL agent under unpredictable sensing noise. With a fixed agent policy, we demonstrate that an optimal adversary to perturb state observations can be found, which is guaranteed to obtain the worst case agent reward. For DRL settings, this leads to a novel empirical adversarial attack to RL agents via a learned adversary that is much stronger than previous ones. To enhance the robustness of an agent, we propose a framework of alternating training with learned adversaries (ATLA), which trains an adversary online together with the agent using policy gradient following the optimal adversarial attack framework. Additionally, inspired by the analysis of state-adversarial Markov decision process (SA-MDP), we show that past states and actions (history) can be useful for learning a robust agent, and we empirically find a LSTM based policy can be more robust under adversaries. Empirical evaluations on a few continuous control environments show that ATLA achieves state-of-the-art performance under strong adversaries. Our code is available at https://github.com/huanzhang12/ATLA_robust_RL. | https://openreview.net/pdf/9a0def4f4b70bbb3d4c3157a3ee5e4110bb9363a.pdf |
Learning from others' mistakes: Avoiding dataset biases without modeling them | https://openreview.net/forum?id=Hf3qXoiNkR | https://openreview.net/forum?id=Hf3qXoiNkR | Victor Sanh,Thomas Wolf,Yonatan Belinkov,Alexander M Rush | ICLR 2021,Poster | State-of-the-art natural language processing (NLP) models often learn to model dataset biases and surface form correlations instead of features that target the intended underlying task. Previous work has demonstrated effective methods to circumvent these issues when knowledge of the bias is available. We consider cases where the bias issues may not be explicitly identified, and show a method for training models that learn to ignore these problematic correlations. Our approach relies on the observation that models with limited capacity primarily learn to exploit biases in the dataset. We can leverage the errors of such limited capacity models to train a more robust model in a product of experts, thus bypassing the need to hand-craft a biased model. We show the effectiveness of this method to retain improvements in out-of-distribution settings even if no particular bias is targeted by the biased model. | https://openreview.net/pdf/85a08fe172298a34963413165b7313bc68c4656a.pdf |
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers | https://openreview.net/forum?id=nVZtXBI6LNn | https://openreview.net/forum?id=nVZtXBI6LNn | Kaidi Xu,Huan Zhang,Shiqi Wang,Yihan Wang,Suman Jana,Xue Lin,Cho-Jui Hsieh | ICLR 2021,Poster | Formal verification of neural networks (NNs) is a challenging and important problem. Existing efficient complete solvers typically require the branch-and-bound (BaB) process, which splits the problem domain into sub-domains and solves each sub-domain using faster but weaker incomplete verifiers, such as Linear Programming (LP) on linearly relaxed sub-domains. In this paper, we propose to use the backward mode linear relaxation based perturbation analysis (LiRPA) to replace LP during the BaB process, which can be efficiently implemented on the typical machine learning accelerators such as GPUs and TPUs. However, unlike LP, LiRPA when applied naively can produce much weaker bounds and even cannot check certain conflicts of sub-domains during splitting, making the entire procedure incomplete after BaB. To address these challenges, we apply a fast gradient based bound tightening procedure combined with batch splits and the design of minimal usage of LP bound procedure, enabling us to effectively use LiRPA on the accelerator hardware for the challenging complete NN verification problem and significantly outperform LP-based approaches. On a single GPU, we demonstrate an order of magnitude speedup compared to existing LP-based approaches. | https://openreview.net/pdf/e8ea009d8faf5987887d2dc7ca2b4d680a2f83dc.pdf |
Self-supervised Adversarial Robustness for the Low-label, High-data Regime | https://openreview.net/forum?id=bgQek2O63w | https://openreview.net/forum?id=bgQek2O63w | Sven Gowal,Po-Sen Huang,Aaron van den Oord,Timothy Mann,Pushmeet Kohli | ICLR 2021,Poster | Recent work discovered that training models to be invariant to adversarial perturbations requires substantially larger datasets than those required for standard classification. Perhaps more surprisingly, these larger datasets can be "mostly" unlabeled. Pseudo-labeling, a technique simultaneously pioneered by four separate and simultaneous works in 2019, has been proposed as a competitive alternative to labeled data for training adversarially robust models. However, when the amount of labeled data decreases, the performance of pseudo-labeling catastrophically drops, thus questioning the theoretical insights put forward by Uesato et al. (2019), which suggest that the sample complexity for learning an adversarially robust model from unlabeled data should match the fully supervised case. We introduce Bootstrap Your Own Robust Latents (BYORL), a self-supervised learning technique based on BYOL for training adversarially robust models. Our method enables us to train robust representations without any labels (reconciling practice with theory). Most notably, this robust representation can be leveraged by a linear classifier to train adversarially robust models, even when the linear classifier is not trained adversarially. We evaluate BYORL and pseudo-labeling on CIFAR-10 and ImageNet and demonstrate that BYORL achieves significantly higher robustness (i.e., models resulting from BYORL are up to two times more accurate). Experiments on CIFAR-10 against $\ell_2$ and $\ell_\infty$ norm-bounded perturbations demonstrate that BYORL achieves near state-of-the-art robustness with as little as 500 labeled examples. We also note that against $\ell_2$ norm-bounded perturbations of size $\epsilon = 128/255$, BYORL surpasses the known state-of-the-art with an accuracy under attack of 77.61% (against 72.91% for the prior art). | https://openreview.net/pdf/f29096120961de72d4ebfd2f2060c34d17b2df3c.pdf |
Modeling the Second Player in Distributionally Robust Optimization | https://openreview.net/forum?id=ZDnzZrTqU9N | https://openreview.net/forum?id=ZDnzZrTqU9N | Paul Michel,Tatsunori Hashimoto,Graham Neubig | ICLR 2021,Poster | Distributionally robust optimization (DRO) provides a framework for training machine learning models that are able to perform well on a collection of related data distributions (the "uncertainty set"). This is done by solving a min-max game: the model is trained to minimize its maximum expected loss among all distributions in the uncertainty set. While careful design of the uncertainty set is critical to the success of the DRO procedure, previous work has been limited to relatively simple alternatives that keep the min-max optimization problem exactly tractable, such as $f$-divergence balls. In this paper, we argue instead for the use of neural generative models to characterize the worst-case distribution, allowing for more flexible and problem-specific selection of the uncertainty set. However, while simple conceptually, this approach poses a number of implementation and optimization challenges. To circumvent these issues, we propose a relaxation of the KL-constrained inner maximization objective that makes the DRO problem more amenable to gradient-based optimization of large scale generative models, and develop model selection heuristics to guide hyper-parameter search. On both toy settings and realistic NLP tasks, we find that the proposed approach yields models that are more robust than comparable baselines. | https://openreview.net/pdf/e31aab007b357e40ee683c0d59d6d939a70c3b2a.pdf |
Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering | https://openreview.net/forum?id=JFKR3WqwyXR | https://openreview.net/forum?id=JFKR3WqwyXR | Calypso Herrera,Florian Krach,Josef Teichmann | ICLR 2021,Poster | Combinations of neural ODEs with recurrent neural networks (RNN), like GRU-ODE-Bayes or ODE-RNN are well suited to model irregularly observed time series. While those models outperform existing discrete-time approaches, no theoretical guarantees for their predictive capabilities are available. Assuming that the irregularly-sampled time series data originates from a continuous stochastic process, the $L^2$-optimal online prediction is the conditional expectation given the currently available information. We introduce the Neural Jump ODE (NJ-ODE) that provides a data-driven approach to learn, continuously in time, the conditional expectation of a stochastic process. Our approach models the conditional expectation between two observations with a neural ODE and jumps whenever a new observation is made. We define a novel training framework, which allows us to prove theoretical guarantees for the first time. In particular, we show that the output of our model converges to the $L^2$-optimal prediction. This can be interpreted as solution to a special filtering problem. We provide experiments showing that the theoretical results also hold empirically. Moreover, we experimentally show that our model outperforms the baselines in more complex learning tasks and give comparisons on real-world datasets. | https://openreview.net/pdf/56667371832a16b261e1e17afd3b1d2f6e9bfb0e.pdf |
Gradient Origin Networks | https://openreview.net/forum?id=0O_cQfw6uEh | https://openreview.net/forum?id=0O_cQfw6uEh | Sam Bond-Taylor,Chris G. Willcocks | ICLR 2021,Poster | This paper proposes a new type of generative model that is able to quickly learn a latent representation without an encoder. This is achieved using empirical Bayes to calculate the expectation of the posterior, which is implemented by initialising a latent vector with zeros, then using the gradient of the log-likelihood of the data with respect to this zero vector as new latent points. The approach has similar characteristics to autoencoders, but with a simpler architecture, and is demonstrated in a variational autoencoder equivalent that permits sampling. This also allows implicit representation networks to learn a space of implicit functions without requiring a hypernetwork, retaining their representation advantages across datasets. The experiments show that the proposed method converges faster, with significantly lower reconstruction error than autoencoders, while requiring half the parameters. | https://openreview.net/pdf/afd0b57d3c44292d9b9855c9f88b7f8f152fc9dd.pdf |
Efficient Generalized Spherical CNNs | https://openreview.net/forum?id=rWZz3sJfCkm | https://openreview.net/forum?id=rWZz3sJfCkm | Oliver Cobb,Christopher G. R. Wallis,Augustine N. Mavor-Parker,Augustin Marignier,Matthew A. Price,Mayeul d'Avezac,Jason McEwen | ICLR 2021,Poster | Many problems across computer vision and the natural sciences require the analysis of spherical data, for which representations may be learned efficiently by encoding equivariance to rotational symmetries. We present a generalized spherical CNN framework that encompasses various existing approaches and allows them to be leveraged alongside each other. The only existing non-linear spherical CNN layer that is strictly equivariant has complexity $\mathcal{O}(C^2L^5)$, where $C$ is a measure of representational capacity and $L$ the spherical harmonic bandlimit. Such a high computational cost often prohibits the use of strictly equivariant spherical CNNs. We develop two new strictly equivariant layers with reduced complexity $\mathcal{O}(CL^4)$ and $\mathcal{O}(CL^3 \log L)$, making larger, more expressive models computationally feasible. Moreover, we adopt efficient sampling theory to achieve further computational savings. We show that these developments allow the construction of more expressive hybrid models that achieve state-of-the-art accuracy and parameter efficiency on spherical benchmark problems. | https://openreview.net/pdf/ac9b9f7c6e9cdda26ee96fc1eb656465da6950e6.pdf |
DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION | https://openreview.net/forum?id=XPZIaotutsD | https://openreview.net/forum?id=XPZIaotutsD | Pengcheng He,Xiaodong Liu,Jianfeng Gao,Weizhu Chen | ICLR 2021,Poster | Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions, respectively. Second, an enhanced mask decoder is used to incorporate absolute positions in the decoding layer to predict the masked tokens in model pre-training. In addition, a new virtual adversarial training method is used for fine-tuning to improve models’
generalization. We show that these techniques significantly improve the efficiency of model pre-training and the performance of both natural language understand(NLU) and natural langauge generation (NLG) downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). Notably, we scale up DeBERTa by training a larger version that consists of 48 Transform layers with 1.5 billion parameters. The significant performance boost makes the single DeBERTa model surpass the human performance on the SuperGLUE benchmark (Wang et al., 2019a) for the first time in terms of macro-average score (89.9 versus 89.8), and the ensemble DeBERTa model sits atop the SuperGLUE leaderboard as of January 6, 2021, outperforming the human baseline by a decent margin (90.3 versus
89.8). The pre-trained DeBERTa models and the source code were released at: https://github.com/microsoft/DeBERTa.
| https://openreview.net/pdf/283448c4c3318a56c7bb21743019e9938f252538.pdf |
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning | https://openreview.net/forum?id=-6vS_4Kfz0 | https://openreview.net/forum?id=-6vS_4Kfz0 | Shauharda Khadka,Estelle Aflalo,Mattias Marder,Avrech Ben-David,Santiago Miret,Shie Mannor,Tamir Hazan,Hanlin Tang,Somdeb Majumdar | ICLR 2021,Poster | For deep neural network accelerators, memory movement is both energetically expensive and can bound computation. Therefore, optimal mapping of tensors to memory hierarchies is critical to performance. The growing complexity of neural networks calls for automated memory mapping instead of manual heuristic approaches; yet the search space of neural network computational graphs have previously been prohibitively large. We introduce Evolutionary Graph Reinforcement Learning (EGRL), a method designed for large search spaces, that combines graph neural networks, reinforcement learning, and evolutionary search. A set of fast, stateless policies guide the evolutionary search to improve its sample-efficiency. We train and validate our approach directly on the Intel NNP-I chip for inference. EGRL outperforms policy-gradient, evolutionary search and dynamic programming baselines on BERT, ResNet-101 and ResNet-50. We additionally achieve 28-78% speed-up compared to the native NNP-I compiler on all three workloads. | https://openreview.net/pdf/04f5290361838504c7aedc9907d8058042fe455e.pdf |
On the geometry of generalization and memorization in deep neural networks | https://openreview.net/forum?id=V8jrrnwGbuc | https://openreview.net/forum?id=V8jrrnwGbuc | Cory Stephenson,suchismita padhy,Abhinav Ganesh,Yue Hui,Hanlin Tang,SueYeon Chung | ICLR 2021,Poster | Understanding how large neural networks avoid memorizing training data is key to explaining their high generalization performance. To examine the structure of when and where memorization occurs in a deep network, we use a recently developed replica-based mean field theoretic geometric analysis method. We find that all layers preferentially learn from examples which share features, and link this behavior to generalization performance. Memorization predominately occurs in the deeper layers, due to decreasing object manifolds’ radius and dimension, whereas early layers are minimally affected. This predicts that generalization can be restored by reverting the final few layer weights to earlier epochs before significant memorization occurred, which is confirmed by the experiments. Additionally, by studying generalization under different model sizes, we reveal the connection between the double descent phenomenon and the underlying model geometry. Finally, analytical analysis shows that networks avoid memorization early in training because close to initialization, the gradient contribution from permuted examples are small. These findings provide quantitative evidence for the structure of memorization across layers of a deep neural network, the drivers for such structure, and its connection to manifold geometric properties.
| https://openreview.net/pdf/4c875dc13f634270639a752914300c6c90ea6977.pdf |
Continual learning in recurrent neural networks | https://openreview.net/forum?id=8xeBUgD8u9 | https://openreview.net/forum?id=8xeBUgD8u9 | Benjamin Ehret,Christian Henning,Maria Cervera,Alexander Meulemans,Johannes Von Oswald,Benjamin F Grewe | ICLR 2021,Poster | While a diverse collection of continual learning (CL) methods has been proposed to prevent catastrophic forgetting, a thorough investigation of their effectiveness for processing sequential data with recurrent neural networks (RNNs) is lacking. Here, we provide the first comprehensive evaluation of established CL methods on a variety of sequential data benchmarks. Specifically, we shed light on the particularities that arise when applying weight-importance methods, such as elastic weight consolidation, to RNNs. In contrast to feedforward networks, RNNs iteratively reuse a shared set of weights and require working memory to process input samples. We show that the performance of weight-importance methods is not directly affected by the length of the processed sequences, but rather by high working memory requirements, which lead to an increased need for stability at the cost of decreased plasticity for learning subsequent tasks. We additionally provide theoretical arguments supporting this interpretation by studying linear RNNs. Our study shows that established CL methods can be successfully ported to the recurrent case, and that a recent regularization approach based on hypernetworks outperforms weight-importance methods, thus emerging as a promising candidate for CL in RNNs. Overall, we provide insights on the differences between CL in feedforward networks and RNNs, while guiding towards effective solutions to tackle CL on sequential data. | https://openreview.net/pdf/c8abb8aebd4518ff958e3cde3bb8c972fecd7cba.pdf |
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching | https://openreview.net/forum?id=01olnfLIbD | https://openreview.net/forum?id=01olnfLIbD | Jonas Geiping,Liam H Fowl,W. Ronny Huang,Wojciech Czaja,Gavin Taylor,Michael Moeller,Tom Goldstein | ICLR 2021,Poster | Data Poisoning attacks modify training data to maliciously control a model trained on such data.
In this work, we focus on targeted poisoning attacks which cause a reclassification of an unmodified test image and as such breach model integrity. We consider a
particularly malicious poisoning attack that is both ``from scratch" and ``clean label", meaning we analyze an attack that successfully works against new, randomly initialized models, and is nearly imperceptible to humans, all while perturbing only a small fraction of the training data.
Previous poisoning attacks against deep neural networks in this setting have been limited in scope and success, working only in simplified settings or being prohibitively expensive for large datasets.
The central mechanism of the new attack is matching the gradient direction of malicious examples. We analyze why this works, supplement with practical considerations. and show its threat to real-world practitioners, finding that it is the first poisoning method to cause targeted misclassification in modern deep networks trained from scratch on a full-sized, poisoned ImageNet dataset.
Finally we demonstrate the limitations of existing defensive strategies against such an attack, concluding that data poisoning is a credible threat, even for large-scale deep learning systems. | https://openreview.net/pdf/3a3c570da85848de52605f6669aae395d063027b.pdf |
Overfitting for Fun and Profit: Instance-Adaptive Data Compression | https://openreview.net/forum?id=oFp8Mx_V5FL | https://openreview.net/forum?id=oFp8Mx_V5FL | Ties van Rozendaal,Iris AM Huijben,Taco Cohen | ICLR 2021,Poster | Neural data compression has been shown to outperform classical methods in terms of $RD$ performance, with results still improving rapidly.
At a high level, neural compression is based on an autoencoder that tries to reconstruct the input instance from a (quantized) latent representation, coupled with a prior that is used to losslessly compress these latents.
Due to limitations on model capacity and imperfect optimization and generalization, such models will suboptimally compress test data in general.
However, one of the great strengths of learned compression is that if the test-time data distribution is known and relatively low-entropy (e.g. a camera watching a static scene, a dash cam in an autonomous car, etc.), the model can easily be finetuned or adapted to this distribution, leading to improved $RD$ performance.
In this paper we take this concept to the extreme, adapting the full model to a single video, and sending model updates (quantized and compressed using a parameter-space prior) along with the latent representation. Unlike previous work, we finetune not only the encoder/latents but the entire model, and - during finetuning - take into account both the effect of model quantization and the additional costs incurred by sending the model updates. We evaluate an image compression model on I-frames (sampled at 2 fps) from videos of the Xiph dataset, and demonstrate that full-model adaptation improves $RD$ performance by ~1 dB, with respect to encoder-only finetuning. | https://openreview.net/pdf/fb86b8ef98e296045da88b73a31804ed7b488f77.pdf |
A Block Minifloat Representation for Training Deep Neural Networks | https://openreview.net/forum?id=6zaTwpNSsQ2 | https://openreview.net/forum?id=6zaTwpNSsQ2 | Sean Fox,Seyedramin Rasoulinezhad,Julian Faraone,david boland,Philip Leong | ICLR 2021,Poster | Training Deep Neural Networks (DNN) with high efficiency can be difficult to achieve with native floating-point representations and commercially available hardware. Specialized arithmetic with custom acceleration offers perhaps the most promising alternative. Ongoing research is trending towards narrow floating-point representations, called minifloats, that pack more operations for a given silicon area and consume less power. In this paper, we introduce Block Minifloat (BM), a new spectrum of minifloat formats capable of training DNNs end-to-end with only 4-8 bit weight, activation and gradient tensors. While standard floating-point representations have two degrees of freedom, via the exponent and mantissa, BM exposes the exponent bias as an additional field for optimization. Crucially, this enables training with fewer exponent bits, yielding dense integer-like hardware for fused multiply-add (FMA) operations. For ResNet trained on ImageNet, 6-bit BM achieves almost no degradation in floating-point accuracy with FMA units that are $4.1\times(23.9\times)$ smaller and consume $2.3\times(16.1\times)$ less energy than FP8 (FP32). Furthermore, our 8-bit BM format matches floating-point accuracy while delivering a higher computational density and faster expected training times. | https://openreview.net/pdf/7ed682ba5c220f98e96984a0b3bb08ed91c59ce7.pdf |
Representation Learning via Invariant Causal Mechanisms | https://openreview.net/forum?id=9p2ekP904Rs | https://openreview.net/forum?id=9p2ekP904Rs | Jovana Mitrovic,Brian McWilliams,Jacob C Walker,Lars Holger Buesing,Charles Blundell | ICLR 2021,Poster | Self-supervised learning has emerged as a strategy to reduce the reliance on costly supervised signal by pretraining representations only using unlabeled data. These methods combine heuristic proxy classification tasks with data augmentations and have achieved significant success, but our theoretical understanding of this success remains limited. In this paper we analyze self-supervised representation learning using a causal framework. We show how data augmentations can be more effectively utilized through explicit invariance constraints on the proxy classifiers employed during pretraining. Based on this, we propose a novel self-supervised objective, Representation Learning via Invariant Causal Mechanisms (ReLIC), that enforces invariant prediction of proxy targets across augmentations through an invariance regularizer which yields improved generalization guarantees. Further, using causality we generalize contrastive learning, a particular kind of self-supervised method, and provide an alternative theoretical explanation for the success of these methods. Empirically, ReLIC significantly outperforms competing methods in terms of robustness and out-of-distribution generalization on ImageNet, while also significantly outperforming these methods on Atari achieving above human-level performance on 51 out of 57 games. | https://openreview.net/pdf/34eb2506b5a0b489bced58ab4bb038ff7356ade7.pdf |
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization | https://openreview.net/forum?id=D_KeYoqCYC | https://openreview.net/forum?id=D_KeYoqCYC | Joshua C Chang,Patrick Fletcher,Jungmin Han,Ted L Chang,Shashaank Vattikuti,Bart Desmet,Ayah Zirikly,Carson C Chow | ICLR 2021,Poster | Dimensionality reduction methods for count data are critical to a wide range of applications in medical informatics and other fields where model interpretability is paramount. For such data, hierarchical Poisson matrix factorization (HPF) and other sparse probabilistic non-negative matrix factorization (NMF) methods are considered to be interpretable generative models. They consist of sparse transformations for decoding their learned representations into predictions. However, sparsity in representation decoding does not necessarily imply sparsity in the encoding of representations from the original data features. HPF is often incorrectly interpreted in the literature as if it possesses encoder sparsity. The distinction between decoder sparsity and encoder sparsity is subtle but important. Due to the lack of encoder sparsity, HPF does not possess the column-clustering property of classical NMF -- the factor loading matrix does not sufficiently define how each factor is formed from the original features. We address this deficiency by self-consistently enforcing encoder sparsity, using a generalized additive model (GAM), thereby allowing one to relate each representation coordinate to a subset of the original data features. In doing so, the method also gains the ability to perform feature selection. We demonstrate our method on simulated data and give an example of how encoder sparsity is of practical use in a concrete application of representing inpatient comorbidities in Medicare patients. | https://openreview.net/pdf/8dec97cd769e293780630c02e65abf11b85674b1.pdf |
Mapping the Timescale Organization of Neural Language Models | https://openreview.net/forum?id=J3OUycKwz- | https://openreview.net/forum?id=J3OUycKwz- | Hsiang-Yun Sherry Chien,Jinhan Zhang,Christopher Honey | ICLR 2021,Poster | In the human brain, sequences of language input are processed within a distributed and hierarchical architecture, in which higher stages of processing encode contextual information over longer timescales. In contrast, in recurrent neural networks which perform natural language processing, we know little about how the multiple timescales of contextual information are functionally organized. Therefore, we applied tools developed in neuroscience to map the “processing timescales” of individual units within a word-level LSTM language model. This timescale-mapping method assigned long timescales to units previously found to track long-range syntactic dependencies. Additionally, the mapping revealed a small subset of the network (less than 15% of units) with long timescales and whose function had not previously been explored. We next probed the functional organization of the network by examining the relationship between the processing timescale of units and their network connectivity. We identified two classes of long-timescale units: “controller” units composed a densely interconnected subnetwork and strongly projected to the rest of the network, while “integrator” units showed the longest timescales in the network, and expressed projection profiles closer to the mean projection profile. Ablating integrator and controller units affected model performance at different positions within a sentence, suggesting distinctive functions of these two sets of units. Finally, we tested the generalization of these results to a character-level LSTM model and models with different architectures. In summary, we demonstrated a model-free technique for mapping the timescale organization in recurrent neural networks, and we applied this method to reveal the timescale and functional organization of neural language models | https://openreview.net/pdf/13cd0af93335a87135e32f629677ce93f146f5bb.pdf |
Neural networks with late-phase weights | https://openreview.net/forum?id=C0qJUx5dxFb | https://openreview.net/forum?id=C0qJUx5dxFb | Johannes Von Oswald,Seijin Kobayashi,Joao Sacramento,Alexander Meulemans,Christian Henning,Benjamin F Grewe | ICLR 2021,Poster | The largely successful method of training neural networks is to learn their weights using some variant of stochastic gradient descent (SGD). Here, we show that the solutions found by SGD can be further improved by ensembling a subset of the weights in late stages of learning. At the end of learning, we obtain back a single model by taking a spatial average in weight space. To avoid incurring increased computational costs, we investigate a family of low-dimensional late-phase weight models which interact multiplicatively with the remaining parameters. Our results show that augmenting standard models with late-phase weights improves generalization in established benchmarks such as CIFAR-10/100, ImageNet and enwik8. These findings are complemented with a theoretical analysis of a noisy quadratic problem which provides a simplified picture of the late phases of neural network learning. | https://openreview.net/pdf/b8fee003035442b69f0891687d5afc8ab42545d4.pdf |
Uncertainty-aware Active Learning for Optimal Bayesian Classifier | https://openreview.net/forum?id=Mu2ZxFctAI | https://openreview.net/forum?id=Mu2ZxFctAI | Guang Zhao,Edward Dougherty,Byung-Jun Yoon,Francis Alexander,Xiaoning Qian | ICLR 2021,Poster | For pool-based active learning, in each iteration a candidate training sample is chosen for labeling by optimizing an acquisition function. In Bayesian classification, expected Loss Reduction~(ELR) methods maximize the expected reduction in the classification error given a new labeled candidate based on a one-step-look-ahead strategy. ELR is the optimal strategy with a single query; however, since such myopic strategies cannot identify the long-term effect of a query on the classification error, ELR may get stuck before reaching the optimal classifier. In this paper, inspired by the mean objective cost of uncertainty (MOCU), a metric quantifying the uncertainty directly affecting the classification error, we propose an acquisition function based on a weighted form of MOCU. Similar to ELR, the proposed method focuses on the reduction of the uncertainty that pertains to the classification error. But unlike any other existing scheme, it provides the critical advantage that the resulting Bayesian active learning algorithm guarantees convergence to the optimal classifier of the true model. We demonstrate its performance with both synthetic and real-world datasets. | https://openreview.net/pdf/afb9e3d9e377ad46e8edc01c8ca00e45787831f8.pdf |
ResNet After All: Neural ODEs and Their Numerical Solution | https://openreview.net/forum?id=HxzSxSxLOJZ | https://openreview.net/forum?id=HxzSxSxLOJZ | Katharina Ott,Prateek Katiyar,Philipp Hennig,Michael Tiemann | ICLR 2021,Poster | A key appeal of the recently proposed Neural Ordinary Differential Equation (ODE) framework is that it seems to provide a continuous-time extension of discrete residual neural networks.
As we show herein, though, trained Neural ODE models actually depend on the specific numerical method used during training.
If the trained model is supposed to be a flow generated from an ODE, it should be possible to choose another numerical solver with equal or smaller numerical error without loss of performance.
We observe that if training relies on a solver with overly coarse discretization, then testing with another solver of equal or smaller numerical error results in a sharp drop in accuracy.
In such cases, the combination of vector field and numerical method cannot be interpreted as a flow generated from an ODE, which arguably poses a fatal breakdown of the Neural ODE concept.
We observe, however, that there exists a critical step size beyond which the training yields a valid ODE vector field.
We propose a method that monitors the behavior of the ODE solver during training to adapt its step size, aiming to ensure a valid ODE without unnecessarily increasing computational cost.
We verify this adaption algorithm on a common bench mark dataset as well as a synthetic dataset.
| https://openreview.net/pdf/b059cce0ad22c05dea6c731336b7fb6d893edab1.pdf |
Generalized Variational Continual Learning | https://openreview.net/forum?id=_IM-AfFhna9 | https://openreview.net/forum?id=_IM-AfFhna9 | Noel Loo,Siddharth Swaroop,Richard E Turner | ICLR 2021,Poster | Continual learning deals with training models on new tasks and datasets in an online fashion. One strand of research has used probabilistic regularization for continual learning, with two of the main approaches in this vein being Online Elastic Weight Consolidation (Online EWC) and Variational Continual Learning (VCL). VCL employs variational inference, which in other settings has been improved empirically by applying likelihood-tempering. We show that applying this modification to VCL recovers Online EWC as a limiting case, allowing for interpolation between the two approaches. We term the general algorithm Generalized VCL (GVCL). In order to mitigate the observed overpruning effect of VI, we take inspiration from a common multi-task architecture, neural networks with task-specific FiLM layers, and find that this addition leads to significant performance gains, specifically for variational methods. In the small-data regime, GVCL strongly outperforms existing baselines. In larger datasets, GVCL with FiLM layers outperforms or is competitive with existing baselines in terms of accuracy, whilst also providing significantly better calibration. | https://openreview.net/pdf/75e7423995a4eb4d239591596556bd1ac05f5e63.pdf |
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models | https://openreview.net/forum?id=a2gqxKDvYys | https://openreview.net/forum?id=a2gqxKDvYys | Justin Bayer,Maximilian Soelch,Atanas Mirchev,Baris Kayalibay,Patrick van der Smagt | ICLR 2021,Poster | Amortised inference enables scalable learning of sequential latent-variable models (LVMs) with the evidence lower bound (ELBO). In this setting, variational posteriors are often only partially conditioned. While the true posteriors depend, e.g., on the entire sequence of observations, approximate posteriors are only informed by past observations. This mimics the Bayesian filter---a mixture of smoothing posteriors. Yet, we show that the ELBO objective forces partially-conditioned amortised posteriors to approximate products of smoothing posteriors instead. Consequently, the learned generative model is compromised. We demonstrate these theoretical findings in three scenarios: traffic flow, handwritten digits, and aerial vehicle dynamics. Using fully-conditioned approximate posteriors, performance improves in terms of generative modelling and multi-step prediction. | https://openreview.net/pdf/3b8adcab7340a271fe4624eabaaa9168eb0c8899.pdf |
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning | https://openreview.net/forum?id=SK7A5pdrgov | https://openreview.net/forum?id=SK7A5pdrgov | Ossama Ahmed,Frederik Träuble,Anirudh Goyal,Alexander Neitz,Manuel Wuthrich,Yoshua Bengio,Bernhard Schölkopf,Stefan Bauer | ICLR 2021,Poster | Despite recent successes of reinforcement learning (RL), it remains a challenge for agents to transfer learned skills to related environments. To facilitate research addressing this problem, we proposeCausalWorld, a benchmark for causal structure and transfer learning in a robotic manipulation environment. The environment is a simulation of an open-source robotic platform, hence offering the possibility of sim-to-real transfer. Tasks consist of constructing 3D shapes from a set of blocks - inspired by how children learn to build complex structures. The key strength of CausalWorld is that it provides a combinatorial family of such tasks with common causal structure and underlying factors (including, e.g., robot and object masses, colors, sizes). The user (or the agent) may intervene on all causal variables, which allows for fine-grained control over how similar different tasks (or task distributions) are. One can thus easily define training and evaluation distributions of a desired difficulty level, targeting a specific form of generalization (e.g., only changes in appearance or object mass). Further, this common parametrization facilitates defining curricula by interpolating between an initial and a target task. While users may define their own task distributions, we present eight meaningful distributions as concrete benchmarks, ranging from simple to very challenging, all of which require long-horizon planning as well as precise low-level motor control. Finally, we provide baseline results for a subset of these tasks on distinct training curricula and corresponding evaluation protocols, verifying the feasibility of the tasks in this benchmark. | https://openreview.net/pdf/824ca65f541287b48a971348ef2dff33ffce0ffd.pdf |
Transformer protein language models are unsupervised structure learners | https://openreview.net/forum?id=fylclEqgvgd | https://openreview.net/forum?id=fylclEqgvgd | Roshan Rao,Joshua Meier,Tom Sercu,Sergey Ovchinnikov,Alexander Rives | ICLR 2021,Poster | Unsupervised contact prediction is central to uncovering physical, structural, and functional constraints for protein structure determination and design. For decades, the predominant approach has been to infer evolutionary constraints from a set of related sequences. In the past year, protein language models have emerged as a potential alternative, but performance has fallen short of state-of-the-art approaches in bioinformatics. In this paper we demonstrate that Transformer attention maps learn contacts from the unsupervised language modeling objective. We find the highest capacity models that have been trained to date already outperform a state-of-the-art unsupervised contact prediction pipeline, suggesting these pipelines can be replaced with a single forward pass of an end-to-end model. | https://openreview.net/pdf/df3dd5269b8d22f21bb9984deb359af57f4dc0e6.pdf |
Neural ODE Processes | https://openreview.net/forum?id=27acGyyI1BY | https://openreview.net/forum?id=27acGyyI1BY | Alexander Norcliffe,Cristian Bodnar,Ben Day,Jacob Moss,Pietro Liò | ICLR 2021,Poster | Neural Ordinary Differential Equations (NODEs) use a neural network to model the instantaneous rate of change in the state of a system. However, despite their apparent suitability for dynamics-governed time-series, NODEs present a few disadvantages. First, they are unable to adapt to incoming data-points, a fundamental requirement for real-time applications imposed by the natural direction of time. Second, time-series are often composed of a sparse set of measurements that could be explained by many possible underlying dynamics. NODEs do not capture this uncertainty. In contrast, Neural Processes (NPs) are a new class of stochastic processes providing uncertainty estimation and fast data-adaptation, but lack an explicit treatment of the flow of time. To address these problems, we introduce Neural ODE Processes (NDPs), a new class of stochastic processes determined by a distribution over Neural ODEs. By maintaining an adaptive data-dependent distribution over the underlying ODE, we show that our model can successfully capture the dynamics of low-dimensional systems from just a few data-points. At the same time, we demonstrate that NDPs scale up to challenging high-dimensional time-series with unknown latent dynamics such as rotating MNIST digits. | https://openreview.net/pdf/cd93774612deb1de0bb3d59b6ecfd4411fbac85f.pdf |
The role of Disentanglement in Generalisation | https://openreview.net/forum?id=qbH974jKUVy | https://openreview.net/forum?id=qbH974jKUVy | Milton Llera Montero,Casimir JH Ludwig,Rui Ponte Costa,Gaurav Malhotra,Jeffrey Bowers | ICLR 2021,Poster | Combinatorial generalisation — the ability to understand and produce novel combinations of familiar elements — is a core capacity of human intelligence that current AI systems struggle with. Recently, it has been suggested that learning disentangled representations may help address this problem. It is claimed that such representations should be able to capture the compositional structure of the world which can then be combined to support combinatorial generalisation. In this study, we systematically tested how the degree of disentanglement affects various forms of generalisation, including two forms of combinatorial generalisation that varied in difficulty. We trained three classes of variational autoencoders (VAEs) on two datasets on an unsupervised task by excluding combinations of generative factors during training. At test time we ask the models to reconstruct the missing combinations in order to measure generalisation performance. Irrespective of the degree of disentanglement, we found that the models supported only weak combinatorial generalisation. We obtained the same outcome when we directly input perfectly disentangled representations as the latents, and when we tested a model on a more complex task that explicitly required independent generative factors to be controlled. While learning disentangled representations does improve interpretability and sample efficiency in some downstream tasks, our results suggest that they are not sufficient for supporting more difficult forms of generalisation. | https://openreview.net/pdf/b2891a422f7bcfc82a95c0587ba5da7e42c473db.pdf |
Learning to live with Dale's principle: ANNs with separate excitatory and inhibitory units | https://openreview.net/forum?id=eU776ZYxEpz | https://openreview.net/forum?id=eU776ZYxEpz | Jonathan Cornford,Damjan Kalajdzievski,Marco Leite,Amélie Lamarquette,Dimitri Michael Kullmann,Blake Aaron Richards | ICLR 2021,Poster | The units in artificial neural networks (ANNs) can be thought of as abstractions of biological neurons, and ANNs are increasingly used in neuroscience research. However, there are many important differences between ANN units and real neurons. One of the most notable is the absence of Dale's principle, which ensures that biological neurons are either exclusively excitatory or inhibitory. Dale's principle is typically left out of ANNs because its inclusion impairs learning. This is problematic, because one of the great advantages of ANNs for neuroscience research is their ability to learn complicated, realistic tasks. Here, by taking inspiration from feedforward inhibitory interneurons in the brain we show that we can develop ANNs with separate populations of excitatory and inhibitory units that learn just as well as standard ANNs. We call these networks Dale's ANNs (DANNs). We present two insights that enable DANNs to learn well: (1) DANNs are related to normalization schemes, and can be initialized such that the inhibition centres and standardizes the excitatory activity, (2) updates to inhibitory neuron parameters should be scaled using corrections based on the Fisher Information matrix. These results demonstrate how ANNs that respect Dale's principle can be built without sacrificing learning performance, which is important for future work using ANNs as models of the brain. The results may also have interesting implications for how inhibitory plasticity in the real brain operates. | https://openreview.net/pdf/4ec70d1b966600fcb426c0ea0982e93dd870f226.pdf |
SALD: Sign Agnostic Learning with Derivatives | https://openreview.net/forum?id=7EDgLu9reQD | https://openreview.net/forum?id=7EDgLu9reQD | Matan Atzmon,Yaron Lipman | ICLR 2021,Poster | Learning 3D geometry directly from raw data, such as point clouds, triangle soups, or unoriented meshes is still a challenging task that feeds many downstream computer vision and graphics applications.
In this paper, we introduce SALD: a method for learning implicit neural representations of shapes directly from raw data. We generalize sign agnostic learning (SAL) to include derivatives: given an unsigned distance function to the input raw data, we advocate a novel sign agnostic regression loss, incorporating both pointwise values and gradients of the unsigned distance function. Optimizing this loss leads to a signed implicit function solution, the zero level set of which is a high quality and valid manifold approximation to the input 3D data. The motivation behind SALD is that incorporating derivatives in a regression loss leads to a lower sample complexity, and consequently better fitting. In addition, we provide empirical evidence, as well as theoretical motivation in 2D that SAL enjoys a minimal surface property, favoring minimal area solutions. More importantly, we are able to show that this property still holds for SALD, i.e., with derivatives included.
We demonstrate the efficacy of SALD for shape space learning on two challenging datasets: ShapeNet that contains inconsistent orientation and non-manifold meshes, and D-Faust that contains raw 3D scans (triangle soups). On both these datasets, we present state-of-the-art results. | https://openreview.net/pdf/8ee2bee9e961323ee6b93586fd34377a074e8889.pdf |
Ringing ReLUs: Harmonic Distortion Analysis of Nonlinear Feedforward Networks | https://openreview.net/forum?id=TaYhv-q1Xit | https://openreview.net/forum?id=TaYhv-q1Xit | Christian H.X. Ali Mehmeti-Göpel,David Hartmann,Michael Wand | ICLR 2021,Poster | In this paper, we apply harmonic distortion analysis to understand the effect of nonlinearities in the spectral domain. Each nonlinear layer creates higher-frequency harmonics, which we call "blueshift", whose magnitude increases with network depth, thereby increasing the “roughness” of the output landscape. Unlike differential models (such as vanishing gradients, sharpness), this provides a more global view of how network architectures behave across larger areas of their parameter domain. For example, the model predicts that residual connections are able to counter the effect by dampening corresponding higher frequency modes. We empirically verify the connection between blueshift and architectural choices, and provide evidence for a connection with trainability. | https://openreview.net/pdf/e4c6e52caf84c69fd30b6bfc6f0936b70f6187a9.pdf |
CoCon: A Self-Supervised Approach for Controlled Text Generation | https://openreview.net/forum?id=VD_ozqvBy4W | https://openreview.net/forum?id=VD_ozqvBy4W | Alvin Chan,Yew-Soon Ong,Bill Pung,Aston Zhang,Jie Fu | ICLR 2021,Poster | Pretrained Transformer-based language models (LMs) display remarkable natural language generation capabilities. With their immense potential, controlling text generation of such LMs is getting attention. While there are studies that seek to control high-level attributes (such as sentiment and topic) of generated text, there is still a lack of more precise control over its content at the word- and phrase-level. Here, we propose Content-Conditioner (CoCon) to control an LM's output text with a content input, at a fine-grained level. In our self-supervised approach, the CoCon block learns to help the LM complete a partially-observed text sequence by conditioning with content inputs that are withheld from the LM. Through experiments, we show that CoCon can naturally incorporate target content into generated texts and control high-level text attributes in a zero-shot manner. | https://openreview.net/pdf/1a1a4e037209eca1776c37a85dff0459bdcad6e8.pdf |
Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech | https://openreview.net/forum?id=o3iritJHLfO | https://openreview.net/forum?id=o3iritJHLfO | Yoonhyung Lee,Joongbo Shin,Kyomin Jung | ICLR 2021,Poster | Although early text-to-speech (TTS) models such as Tacotron 2 have succeeded in generating human-like speech, their autoregressive architectures have several limitations: (1) They require a lot of time to generate a mel-spectrogram consisting of hundreds of steps. (2) The autoregressive speech generation shows a lack of robustness due to its error propagation property. In this paper, we propose a novel non-autoregressive TTS model called BVAE-TTS, which eliminates the architectural limitations and generates a mel-spectrogram in parallel. BVAE-TTS adopts a bidirectional-inference variational autoencoder (BVAE) that learns hierarchical latent representations using both bottom-up and top-down paths to increase its expressiveness. To apply BVAE to TTS, we design our model to utilize text information via an attention mechanism. By using attention maps that BVAE-TTS generates, we train a duration predictor so that the model uses the predicted duration of each phoneme at inference. In experiments conducted on LJSpeech dataset, we show that our model generates a mel-spectrogram 27 times faster than Tacotron 2 with similar speech quality. Furthermore, our BVAE-TTS outperforms Glow-TTS, which is one of the state-of-the-art non-autoregressive TTS models, in terms of both speech quality and inference speed while having 58% fewer parameters. | https://openreview.net/pdf/db03d769745da96b32be80606e358d64a7641d2b.pdf |
Learning continuous-time PDEs from sparse data with graph neural networks | https://openreview.net/forum?id=aUX5Plaq7Oy | https://openreview.net/forum?id=aUX5Plaq7Oy | Valerii Iakovlev,Markus Heinonen,Harri Lähdesmäki | ICLR 2021,Poster | The behavior of many dynamical systems follow complex, yet still unknown partial differential equations (PDEs). While several machine learning methods have been proposed to learn PDEs directly from data, previous methods are limited to discrete-time approximations or make the limiting assumption of the observations arriving at regular grids. We propose a general continuous-time differential model for dynamical systems whose governing equations are parameterized by message passing graph neural networks. The model admits arbitrary space and time discretizations, which removes constraints on the locations of observation points and time intervals between the observations. The model is trained with continuous-time adjoint method enabling efficient neural PDE inference. We demonstrate the model's ability to work with unstructured grids, arbitrary time steps, and noisy observations. We compare our method with existing approaches on several well-known physical systems that involve first and higher-order PDEs with state-of-the-art predictive performance. | https://openreview.net/pdf/65985773a2d3e7c1e7d121a48729c30c9a007745.pdf |
NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition | https://openreview.net/forum?id=CU0APx9LMaL | https://openreview.net/forum?id=CU0APx9LMaL | Abhinav Mehrotra,Alberto Gil C. P. Ramos,Sourav Bhattacharya,Łukasz Dudziak,Ravichander Vipperla,Thomas Chau,Mohamed S Abdelfattah,Samin Ishtiaq,Nicholas Donald Lane | ICLR 2021,Poster | Powered by innovations in novel architecture design, noise tolerance techniques and increasing model capacity, Automatic Speech Recognition (ASR) has made giant strides in reducing word-error-rate over the past decade. ASR models are often trained with tens of thousand hours of high quality speech data to produce state-of-the-art (SOTA) results. Industry-scale ASR model training thus remains computationally heavy and time-consuming, and consequently has attracted little attention in adopting automatic techniques. On the other hand, Neural Architecture Search (NAS) has gained a lot of interest in the recent years thanks to its successes in discovering efficient architectures, often outperforming handcrafted alternatives. However, by changing the standard training process into a bi-level optimisation problem, NAS approaches often require significantly more time and computational power compared to single-model training, and at the same time increase complexity of the overall process. As a result, NAS has been predominately applied to problems which do not require as extensive training as ASR, and even then reproducibility of NAS algorithms is often problematic. Lately, a number of benchmark datasets has been introduced to address reproducibility issues by pro- viding NAS researchers with information about performance of different models obtained through exhaustive evaluation. However, these datasets focus mainly on computer vision and NLP tasks and thus suffer from limited coverage of application domains. In order to increase diversity in the existing NAS benchmarks, and at the same time provide systematic study of the effects of architectural choices for ASR, we release NAS-Bench-ASR – the first NAS benchmark for ASR models. The dataset consists of 8, 242 unique models trained on the TIMIT audio dataset for three different target epochs, and each starting from three different initializations. The dataset also includes runtime measurements of all the models on a diverse set of hardware platforms. Lastly, we show that identified good cell structures in our search space for TIMIT transfer well to a much larger LibriSpeech dataset. | https://openreview.net/pdf/77fdff265261021a568029907ab02c0f1f6e4639.pdf |
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks | https://openreview.net/forum?id=ULQdiUTHe3y | https://openreview.net/forum?id=ULQdiUTHe3y | Jan Schuchardt,Aleksandar Bojchevski,Johannes Gasteiger,Stephan Günnemann | ICLR 2021,Poster | In tasks like node classification, image segmentation, and named-entity recognition we have a classifier that simultaneously outputs multiple predictions (a vector of labels) based on a single input, i.e. a single graph, image, or document respectively. Existing adversarial robustness certificates consider each prediction independently and are thus overly pessimistic for such tasks. They implicitly assume that an adversary can use different perturbed inputs to attack different predictions, ignoring the fact that we have a single shared input. We propose the first collective robustness certificate which computes the number of predictions that are simultaneously guaranteed to remain stable under perturbation, i.e. cannot be attacked. We focus on Graph Neural Networks and leverage their locality property - perturbations only affect the predictions in a close neighborhood - to fuse multiple single-node certificates into a drastically stronger collective certificate. For example, on the Citeseer dataset our collective certificate for node classification increases the average number of certifiable feature perturbations from $7$ to $351$.
| https://openreview.net/pdf/23b393f2d9e53b0dd38356295f14b0980ac27b18.pdf |
Adversarially Guided Actor-Critic | https://openreview.net/forum?id=_mQp5cr_iNy | https://openreview.net/forum?id=_mQp5cr_iNy | Yannis Flet-Berliac,Johan Ferret,Olivier Pietquin,Philippe Preux,Matthieu Geist | ICLR 2021,Poster | Despite definite success in deep reinforcement learning problems, actor-critic algorithms are still confronted with sample inefficiency in complex environments, particularly in tasks where efficient exploration is a bottleneck. These methods consider a policy (the actor) and a value function (the critic) whose respective losses are built using different motivations and approaches. This paper introduces a third protagonist: the adversary. While the adversary mimics the actor by minimizing the KL-divergence between their respective action distributions, the actor, in addition to learning to solve the task, tries to differentiate itself from the adversary predictions. This novel objective stimulates the actor to follow strategies that could not have been correctly predicted from previous trajectories, making its behavior innovative in tasks where the reward is extremely rare. Our experimental analysis shows that the resulting Adversarially Guided Actor-Critic (AGAC) algorithm leads to more exhaustive exploration. Notably, AGAC outperforms current state-of-the-art methods on a set of various hard-exploration and procedurally-generated tasks. | https://openreview.net/pdf/14e743d36a73f6572d0a514054121457cc066814.pdf |
Training independent subnetworks for robust prediction | https://openreview.net/forum?id=OGg9XnKxFAH | https://openreview.net/forum?id=OGg9XnKxFAH | Marton Havasi,Rodolphe Jenatton,Stanislav Fort,Jeremiah Zhe Liu,Jasper Snoek,Balaji Lakshminarayanan,Andrew Mingbo Dai,Dustin Tran | ICLR 2021,Poster | Recent approaches to efficiently ensemble neural networks have shown that strong robustness and uncertainty performance can be achieved with a negligible gain in parameters over the original network. However, these methods still require multiple forward passes for prediction, leading to a significant runtime cost. In this work, we show a surprising result:
the benefits of using multiple predictions can be achieved 'for free' under a single model's forward pass. In particular, we show that, using a multi-input multi-output (MIMO) configuration, one can utilize a single model's capacity to train multiple subnetworks that independently learn the task at hand. By ensembling the predictions made by the subnetworks, we improve model robustness without increasing compute. We observe a significant improvement in negative log-likelihood, accuracy, and calibration error on CIFAR10, CIFAR100, ImageNet, and their out-of-distribution variants compared to previous methods. | https://openreview.net/pdf/685ca0ecb292949cd9ede19a51143b8505e9b854.pdf |
Grounding Language to Autonomously-Acquired Skills via Goal Generation | https://openreview.net/forum?id=chPj_I5KMHG | https://openreview.net/forum?id=chPj_I5KMHG | Ahmed Akakzia,Cédric Colas,Pierre-Yves Oudeyer,Mohamed CHETOUANI,Olivier Sigaud | ICLR 2021,Poster | We are interested in the autonomous acquisition of repertoires of skills. Language-conditioned reinforcement learning (LC-RL) approaches are great tools in this quest, as they allow to express abstract goals as sets of constraints on the states. However, most LC-RL agents are not autonomous and cannot learn without external instructions and feedback. Besides, their direct language condition cannot account for the goal-directed behavior of pre-verbal infants and strongly limits the expression of behavioral diversity for a given language input. To resolve these issues, we propose a new conceptual approach to language-conditioned RL: the Language-Goal-Behavior architecture (LGB). LGB decouples skill learning and language grounding via an intermediate semantic representation of the world. To showcase the properties of LGB, we present a specific implementation called DECSTR. DECSTR is an intrinsically motivated learning agent endowed with an innate semantic representation describing spatial relations between physical objects. In a first stage G -> B, it freely explores its environment and targets self-generated semantic configurations. In a second stage (L -> G), it trains a language-conditioned goal generator to generate semantic goals that match the constraints expressed in language-based inputs. We showcase the additional properties of LGB w.r.t. both an end-to-end LC-RL approach and a similar approach leveraging non-semantic, continuous intermediate representations. Intermediate semantic representations help satisfy language commands in a diversity of ways, enable strategy switching after a failure and facilitate language grounding. | https://openreview.net/pdf/d347c6eea558834f6b578284633cc4f3f1a0c0a9.pdf |
Hopfield Networks is All You Need | https://openreview.net/forum?id=tL89RnzIiCd | https://openreview.net/forum?id=tL89RnzIiCd | Hubert Ramsauer,Bernhard Schäfl,Johannes Lehner,Philipp Seidl,Michael Widrich,Lukas Gruber,Markus Holzleitner,Thomas Adler,David Kreil,Michael K Kopp,Günter Klambauer,Johannes Brandstetter,Sepp Hochreiter | ICLR 2021,Poster | We introduce a modern Hopfield network with continuous states and a corresponding update rule. The new Hopfield network can store exponentially (with the dimension of the associative space) many patterns, retrieves the pattern with one update, and has exponentially small retrieval errors. It has three types of energy minima (fixed points of the update): (1) global fixed point averaging over all patterns, (2) metastable states averaging over a subset of patterns, and (3) fixed points which store a single pattern. The new update rule is equivalent to the attention mechanism used in transformers. This equivalence enables a characterization of the heads of transformer models. These heads perform in the first layers preferably global averaging and in higher layers partial averaging via metastable states. The new modern Hopfield network can be integrated into deep learning architectures as layers to allow the storage of and access to raw input data, intermediate results, or learned prototypes.
These Hopfield layers enable new ways of deep learning, beyond fully-connected, convolutional, or recurrent networks, and provide pooling, memory, association, and attention mechanisms. We demonstrate the broad applicability of the Hopfield layers
across various domains. Hopfield layers improved state-of-the-art on three out of four considered multiple instance learning problems as well as on immune repertoire classification with several hundreds of thousands of instances. On the UCI benchmark collections of small classification tasks, where deep learning methods typically struggle, Hopfield layers yielded a new state-of-the-art when compared to different machine learning methods. Finally, Hopfield layers achieved state-of-the-art on two drug design datasets. The implementation is available at: \url{https://github.com/ml-jku/hopfield-layers} | https://openreview.net/pdf/4dfbed3a6ececb7282dfef90fd6c03812ae0da7b.pdf |
Differentiable Trust Region Layers for Deep Reinforcement Learning | https://openreview.net/forum?id=qYZD-AO1Vn | https://openreview.net/forum?id=qYZD-AO1Vn | Fabian Otto,Philipp Becker,Vien Anh Ngo,Hanna Carolin Maria Ziesche,Gerhard Neumann | ICLR 2021,Poster | Trust region methods are a popular tool in reinforcement learning as they yield robust policy updates in continuous and discrete action spaces. However, enforcing such trust regions in deep reinforcement learning is difficult. Hence, many approaches, such as Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO), are based on approximations. Due to those approximations, they violate the constraints or fail to find the optimal solution within the trust region. Moreover, they are difficult to implement, often lack sufficient exploration, and have been shown to depend on seemingly unrelated implementation choices. In this work, we propose differentiable neural network layers to enforce trust regions for deep Gaussian policies via closed-form projections. Unlike existing methods, those layers formalize trust regions for each state individually and can complement existing reinforcement learning algorithms. We derive trust region projections based on the Kullback-Leibler divergence, the Wasserstein L2 distance, and the Frobenius norm for Gaussian distributions. We empirically demonstrate that those projection layers achieve similar or better results than existing methods while being almost agnostic to specific implementation choices. The code is available at https://git.io/Jthb0.
| https://openreview.net/pdf/16d7f0bd8f02047e8ca8dbcf7a7f000f5d685020.pdf |
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ICLR 2021 International Conference on Learning Representations 2021 Accepted Paper Meta Info Dataset
This dataset is collect from the ICLR 2021 OpenReview website (https://openreview.net/group?id=ICLR.cc/2021/Conference#tab-accept-oral) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/iclr2021). For researchers who are interested in doing analysis of ICLR 2021 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the ICLR 2021 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.
Meta Information of Json File
{
"title": "On the mapping between Hopfield networks and Restricted Boltzmann Machines",
"url": "https://openreview.net/forum?id=RGJbergVIoO",
"detail_url": "https://openreview.net/forum?id=RGJbergVIoO",
"authors": "Matthew Smart,Anton Zilman",
"tags": "ICLR 2021,Oral",
"abstract": "Hopfield networks (HNs) and Restricted Boltzmann Machines (RBMs) are two important models at the interface of statistical physics, machine learning, and neuroscience. Recently, there has been interest in the relationship between HNs and RBMs, due to their similarity under the statistical mechanics formalism. An exact mapping between HNs and RBMs has been previously noted for the special case of orthogonal (\u201cuncorrelated\u201d) encoded patterns. We present here an exact mapping in the case of correlated pattern HNs, which are more broadly applicable to existing datasets. Specifically, we show that any HN with $N$ binary variables and $p<N$ potentially correlated binary patterns can be transformed into an RBM with $N$ binary visible variables and $p$ gaussian hidden variables. We outline the conditions under which the reverse mapping exists, and conduct experiments on the MNIST dataset which suggest the mapping provides a useful initialization to the RBM weights. We discuss extensions, the potential importance of this correspondence for the training of RBMs, and for understanding the performance of feature extraction methods which utilize RBMs.",
"pdf": "https://openreview.net/pdf/3a9204f4495810f86acf886d14ee022a31d7b863.pdf"
}
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