instruction
stringclasses 1
value | input
stringlengths 260
2.07k
| output
stringclasses 10
values |
|---|---|---|
What field is the article from? | Title: XAI for time-series classification leveraging image highlight methods
Abstract: Although much work has been done on explainability in the computer vision and
natural language processing (NLP) fields, there is still much work to be done
to explain methods applied to time series as time series by nature can not be
understood at first sight. In this paper, we present a Deep Neural Network
(DNN) in a teacher-student architecture (distillation model) that offers
interpretability in time-series classification tasks. The explainability of our
approach is based on transforming the time series to 2D plots and applying
image highlight methods (such as LIME and GradCam), making the predictions
interpretable. At the same time, the proposed approach offers increased
accuracy competing with the baseline model with the trade-off of increasing the
training time. | Machine Learning |
What field is the article from? | Title: Continuous 16-bit Training: Accelerating 32-bit Pre-Trained Neural Networks
Abstract: In the field of deep learning, the prevalence of models initially trained
with 32-bit precision is a testament to its robustness and accuracy. However,
the continuous evolution of these models often demands further training, which
can be resource-intensive. This study introduces a novel approach where we
continue the training of these pre-existing 32-bit models using 16-bit
precision. This technique not only caters to the need for efficiency in
computational resources but also significantly improves the speed of additional
training phases. By adopting 16-bit precision for ongoing training, we are able
to substantially decrease memory requirements and computational burden, thereby
accelerating the training process in a resource-limited setting. Our
experiments show that this method maintains the high standards of accuracy set
by the original 32-bit training while providing a much-needed boost in training
speed. This approach is especially pertinent in today's context, where most
models are initially trained in 32-bit and require periodic updates and
refinements. The findings from our research suggest that this strategy of
16-bit continuation training can be a key solution for sustainable and
efficient deep learning, offering a practical way to enhance pre-trained models
rapidly and in a resource-conscious manner. | Machine Learning |
What field is the article from? | Title: PartSLIP++: Enhancing Low-Shot 3D Part Segmentation via Multi-View Instance Segmentation and Maximum Likelihood Estimation
Abstract: Open-world 3D part segmentation is pivotal in diverse applications such as
robotics and AR/VR. Traditional supervised methods often grapple with limited
3D data availability and struggle to generalize to unseen object categories.
PartSLIP, a recent advancement, has made significant strides in zero- and
few-shot 3D part segmentation. This is achieved by harnessing the capabilities
of the 2D open-vocabulary detection module, GLIP, and introducing a heuristic
method for converting and lifting multi-view 2D bounding box predictions into
3D segmentation masks. In this paper, we introduce PartSLIP++, an enhanced
version designed to overcome the limitations of its predecessor. Our approach
incorporates two major improvements. First, we utilize a pre-trained 2D
segmentation model, SAM, to produce pixel-wise 2D segmentations, yielding more
precise and accurate annotations than the 2D bounding boxes used in PartSLIP.
Second, PartSLIP++ replaces the heuristic 3D conversion process with an
innovative modified Expectation-Maximization algorithm. This algorithm
conceptualizes 3D instance segmentation as unobserved latent variables, and
then iteratively refines them through an alternating process of 2D-3D matching
and optimization with gradient descent. Through extensive evaluations, we show
that PartSLIP++ demonstrates better performance over PartSLIP in both low-shot
3D semantic and instance-based object part segmentation tasks. Code released at
https://github.com/zyc00/PartSLIP2. | Computer Vision |
What field is the article from? | Title: Robot Skill Generalization via Keypoint Integrated Soft Actor-Critic Gaussian Mixture Models
Abstract: A long-standing challenge for a robotic manipulation system operating in
real-world scenarios is adapting and generalizing its acquired motor skills to
unseen environments. We tackle this challenge employing hybrid skill models
that integrate imitation and reinforcement paradigms, to explore how the
learning and adaptation of a skill, along with its core grounding in the scene
through a learned keypoint, can facilitate such generalization. To that end, we
develop Keypoint Integrated Soft Actor-Critic Gaussian Mixture Models (KIS-GMM)
approach that learns to predict the reference of a dynamical system within the
scene as a 3D keypoint, leveraging visual observations obtained by the robot's
physical interactions during skill learning. Through conducting comprehensive
evaluations in both simulated and real-world environments, we show that our
method enables a robot to gain a significant zero-shot generalization to novel
environments and to refine skills in the target environments faster than
learning from scratch. Importantly, this is achieved without the need for new
ground truth data. Moreover, our method effectively copes with scene
displacements. | Robotics |
What field is the article from? | Title: "Do it my way!": Impact of Customizations on Trust perceptions in Human-Robot Collaboration
Abstract: Trust has been shown to be a key factor in effective human-robot
collaboration. In the context of assistive robotics, the effect of trust
factors on human experience is further pronounced. Personalization of assistive
robots is an orthogonal factor positively correlated with robot adoption and
user perceptions. In this work, we investigate the relationship between these
factors through a within-subjects study (N=17). We provide different levels of
customization possibilities over baseline autonomous robot behavior and
investigate its impact on trust. Our findings indicate that increased levels of
customization was associated with higher trust and comfort perceptions. The
assistive robot design process can benefit significantly from our insights for
designing trustworthy and customized robots. | Robotics |
What field is the article from? | Title: Scale-Dropout: Estimating Uncertainty in Deep Neural Networks Using Stochastic Scale
Abstract: Uncertainty estimation in Neural Networks (NNs) is vital in improving
reliability and confidence in predictions, particularly in safety-critical
applications. Bayesian Neural Networks (BayNNs) with Dropout as an
approximation offer a systematic approach to quantifying uncertainty, but they
inherently suffer from high hardware overhead in terms of power, memory, and
computation. Thus, the applicability of BayNNs to edge devices with limited
resources or to high-performance applications is challenging. Some of the
inherent costs of BayNNs can be reduced by accelerating them in hardware on a
Computation-In-Memory (CIM) architecture with spintronic memories and
binarizing their parameters. However, numerous stochastic units are required to
implement conventional dropout-based BayNN. In this paper, we propose the Scale
Dropout, a novel regularization technique for Binary Neural Networks (BNNs),
and Monte Carlo-Scale Dropout (MC-Scale Dropout)-based BayNNs for efficient
uncertainty estimation. Our approach requires only one stochastic unit for the
entire model, irrespective of the model size, leading to a highly scalable
Bayesian NN. Furthermore, we introduce a novel Spintronic memory-based CIM
architecture for the proposed BayNN that achieves more than $100\times$ energy
savings compared to the state-of-the-art. We validated our method to show up to
a $1\%$ improvement in predictive performance and superior uncertainty
estimates compared to related works. | Machine Learning |
What field is the article from? | Title: Honeybee: Locality-enhanced Projector for Multimodal LLM
Abstract: In Multimodal Large Language Models (MLLMs), a visual projector plays a
crucial role in bridging pre-trained vision encoders with LLMs, enabling
profound visual understanding while harnessing the LLMs' robust capabilities.
Despite the importance of the visual projector, it has been relatively less
explored. In this study, we first identify two essential projector properties:
(i) flexibility in managing the number of visual tokens, crucial for MLLMs'
overall efficiency, and (ii) preservation of local context from visual
features, vital for spatial understanding. Based on these findings, we propose
a novel projector design that is both flexible and locality-enhanced,
effectively satisfying the two desirable properties. Additionally, we present
comprehensive strategies to effectively utilize multiple and multifaceted
instruction datasets. Through extensive experiments, we examine the impact of
individual design choices. Finally, our proposed MLLM, Honeybee, remarkably
outperforms previous state-of-the-art methods across various benchmarks,
including MME, MMBench, SEED-Bench, and LLaVA-Bench, achieving significantly
higher efficiency. Code and models are available at
https://github.com/kakaobrain/honeybee. | Computer Vision |
What field is the article from? | Title: Large Language Models for Mathematicians
Abstract: Large language models (LLMs) such as ChatGPT have received immense interest
for their general-purpose language understanding and, in particular, their
ability to generate high-quality text or computer code. For many professions,
LLMs represent an invaluable tool that can speed up and improve the quality of
work. In this note, we discuss to what extent they can aid professional
mathematicians. We first provide a mathematical description of the transformer
model used in all modern language models. Based on recent studies, we then
outline best practices and potential issues and report on the mathematical
abilities of language models. Finally, we shed light on the potential of LMMs
to change how mathematicians work. | Computational Linguistics |
What field is the article from? | Title: Graph Convolutional Networks for Complex Traffic Scenario Classification
Abstract: A scenario-based testing approach can reduce the time required to obtain
statistically significant evidence of the safety of Automated Driving Systems
(ADS). Identifying these scenarios in an automated manner is a challenging
task. Most methods on scenario classification do not work for complex scenarios
with diverse environments (highways, urban) and interaction with other traffic
agents. This is mirrored in their approaches which model an individual vehicle
in relation to its environment, but neglect the interaction between multiple
vehicles (e.g. cut-ins, stationary lead vehicle). Furthermore, existing
datasets lack diversity and do not have per-frame annotations to accurately
learn the start and end time of a scenario. We propose a method for complex
traffic scenario classification that is able to model the interaction of a
vehicle with the environment, as well as other agents. We use Graph
Convolutional Networks to model spatial and temporal aspects of these
scenarios. Expanding the nuScenes and Argoverse 2 driving datasets, we
introduce a scenario-labeled dataset, which covers different driving
environments and is annotated per frame. Training our method on this dataset,
we present a promising baseline for future research on per-frame complex
scenario classification. | Computer Vision |
What field is the article from? | Title: Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves
Abstract: Misunderstandings arise not only in interpersonal communication but also
between humans and Large Language Models (LLMs). Such discrepancies can make
LLMs interpret seemingly unambiguous questions in unexpected ways, yielding
incorrect responses. While it is widely acknowledged that the quality of a
prompt, such as a question, significantly impacts the quality of the response
provided by LLMs, a systematic method for crafting questions that LLMs can
better comprehend is still underdeveloped. In this paper, we present a method
named `Rephrase and Respond' (RaR), which allows LLMs to rephrase and expand
questions posed by humans and provide responses in a single prompt. This
approach serves as a simple yet effective prompting method for improving
performance. We also introduce a two-step variant of RaR, where a rephrasing
LLM first rephrases the question and then passes the original and rephrased
questions together to a different responding LLM. This facilitates the
effective utilization of rephrased questions generated by one LLM with another.
Our experiments demonstrate that our methods significantly improve the
performance of different models across a wide range to tasks. We further
provide a comprehensive comparison between RaR and the popular Chain-of-Thought
(CoT) methods, both theoretically and empirically. We show that RaR is
complementary to CoT and can be combined with CoT to achieve even better
performance. Our work not only contributes to enhancing LLM performance
efficiently and effectively but also sheds light on a fair evaluation of LLM
capabilities. Data and codes are available at
https://github.com/uclaml/Rephrase-and-Respond. | Computational Linguistics |
What field is the article from? | Title: Protecting Publicly Available Data With Machine Learning Shortcuts
Abstract: Machine-learning (ML) shortcuts or spurious correlations are artifacts in
datasets that lead to very good training and test performance but severely
limit the model's generalization capability. Such shortcuts are insidious
because they go unnoticed due to good in-domain test performance. In this
paper, we explore the influence of different shortcuts and show that even
simple shortcuts are difficult to detect by explainable AI methods. We then
exploit this fact and design an approach to defend online databases against
crawlers: providers such as dating platforms, clothing manufacturers, or used
car dealers have to deal with a professionalized crawling industry that grabs
and resells data points on a large scale. We show that a deterrent can be
created by deliberately adding ML shortcuts. Such augmented datasets are then
unusable for ML use cases, which deters crawlers and the unauthorized use of
data from the internet. Using real-world data from three use cases, we show
that the proposed approach renders such collected data unusable, while the
shortcut is at the same time difficult to notice in human perception. Thus, our
proposed approach can serve as a proactive protection against illegitimate data
crawling. | Artificial Intelligence |
What field is the article from? | Title: Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images
Abstract: In the last years, the weakly supervised paradigm of multiple instance
learning (MIL) has become very popular in many different areas. A paradigmatic
example is computational pathology, where the lack of patch-level labels for
whole-slide images prevents the application of supervised models. Probabilistic
MIL methods based on Gaussian Processes (GPs) have obtained promising results
due to their excellent uncertainty estimation capabilities. However, these are
general-purpose MIL methods that do not take into account one important fact:
in (histopathological) images, the labels of neighboring patches are expected
to be correlated. In this work, we extend a state-of-the-art GP-based MIL
method, which is called VGPMIL-PR, to exploit such correlation. To do so, we
develop a novel coupling term inspired by the statistical physics Ising model.
We use variational inference to estimate all the model parameters.
Interestingly, the VGPMIL-PR formulation is recovered when the weight that
regulates the strength of the Ising term vanishes. The performance of the
proposed method is assessed in two real-world problems of prostate cancer
detection. We show that our model achieves better results than other
state-of-the-art probabilistic MIL methods. We also provide different
visualizations and analysis to gain insights into the influence of the novel
Ising term. These insights are expected to facilitate the application of the
proposed model to other research areas. | Computer Vision |
What field is the article from? | Title: Exploring Social Bias in Downstream Applications of Text-to-Image Foundation Models
Abstract: Text-to-image diffusion models have been adopted into key commercial
workflows, such as art generation and image editing. Characterising the
implicit social biases they exhibit, such as gender and racial stereotypes, is
a necessary first step in avoiding discriminatory outcomes. While existing
studies on social bias focus on image generation, the biases exhibited in
alternate applications of diffusion-based foundation models remain
under-explored. We propose methods that use synthetic images to probe two
applications of diffusion models, image editing and classification, for social
bias. Using our methodology, we uncover meaningful and significant
inter-sectional social biases in \textit{Stable Diffusion}, a state-of-the-art
open-source text-to-image model. Our findings caution against the uninformed
adoption of text-to-image foundation models for downstream tasks and services. | Computers and Society |
What field is the article from? | Title: er.autopilot 1.0: The Full Autonomous Stack for Oval Racing at High Speeds
Abstract: The Indy Autonomous Challenge (IAC) brought together for the first time in
history nine autonomous racing teams competing at unprecedented speed and in
head-to-head scenario, using independently developed software on open-wheel
racecars. This paper presents the complete software architecture used by team
TII EuroRacing (TII-ER), covering all the modules needed to avoid static
obstacles, perform active overtakes and reach speeds above 75 m/s (270 km/h).
In addition to the most common modules related to perception, planning, and
control, we discuss the approaches used for vehicle dynamics modelling,
simulation, telemetry, and safety. Overall results and the performance of each
module are described, as well as the lessons learned during the first two
events of the competition on oval tracks, where the team placed respectively
second and third. | Robotics |
What field is the article from? | Title: Alignment for Honesty
Abstract: Recent research has made significant strides in applying alignment techniques
to enhance the helpfulness and harmlessness of large language models (LLMs) in
accordance with human intentions. In this paper, we argue for the importance of
alignment for honesty, ensuring that LLMs proactively refuse to answer
questions when they lack knowledge, while still not being overly conservative.
However, a pivotal aspect of alignment for honesty involves discerning the
limits of an LLM's knowledge, which is far from straightforward. This challenge
demands comprehensive solutions in terms of metric development, benchmark
creation, and training methodologies. In this paper, we address these
challenges by first establishing a precise problem definition and defining
``honesty'' inspired by the Analects of Confucius. This serves as a cornerstone
for developing metrics that effectively measure an LLM's honesty by quantifying
its progress post-alignment. Furthermore, we introduce a flexible training
framework which is further instantiated by several efficient fine-tuning
techniques that emphasize honesty without sacrificing performance on other
tasks. Our extensive experiments reveal that these aligned models show a marked
increase in honesty, as indicated by our proposed metrics. We open-source a
wealth of resources to facilitate future research at
https://github.com/GAIR-NLP/alignment-for-honesty, including honesty-aligned
models, training and evaluation datasets for honesty alignment, concept
glossary, as well as all relevant source code. | Computational Linguistics |
What field is the article from? | Title: Reducing Spatial Fitting Error in Distillation of Denoising Diffusion Models
Abstract: Denoising Diffusion models have exhibited remarkable capabilities in image
generation. However, generating high-quality samples requires a large number of
iterations. Knowledge distillation for diffusion models is an effective method
to address this limitation with a shortened sampling process but causes
degraded generative quality. Based on our analysis with bias-variance
decomposition and experimental observations, we attribute the degradation to
the spatial fitting error occurring in the training of both the teacher and
student model. Accordingly, we propose $\textbf{S}$patial
$\textbf{F}$itting-$\textbf{E}$rror $\textbf{R}$eduction
$\textbf{D}$istillation model ($\textbf{SFERD}$). SFERD utilizes attention
guidance from the teacher model and a designed semantic gradient predictor to
reduce the student's fitting error. Empirically, our proposed model facilitates
high-quality sample generation in a few function evaluations. We achieve an FID
of 5.31 on CIFAR-10 and 9.39 on ImageNet 64$\times$64 with only one step,
outperforming existing diffusion methods. Our study provides a new perspective
on diffusion distillation by highlighting the intrinsic denoising ability of
models. | Computer Vision |
What field is the article from? | Title: ERASER: Machine Unlearning in MLaaS via an Inference Serving-Aware Approach
Abstract: Over the past few years, Machine Learning-as-a-Service (MLaaS) has received a
surging demand for supporting Machine Learning-driven services to offer
revolutionized user experience across diverse application areas. MLaaS provides
inference service with low inference latency to application users based on an
ML model trained using a dataset collected from numerous individual data
owners. Recently, for the sake of data owners' privacy and to comply with the
"right to be forgotten (RTBF)" as enacted by data protection legislation, many
machine unlearning methods have been proposed to remove data owners' data from
trained models upon their unlearning requests. However, despite their promising
efficiency, almost all existing machine unlearning methods handle unlearning
requests in a manner that is independent of inference requests, which
unfortunately introduces new security and privacy vulnerabilities for machine
unlearning in MLaaS. In this paper, we propose the ERASER framework for machinE
unleaRning in MLaAS via an inferencE seRving-aware approach. ERASER proposes a
novel certified inference consistency mechanism that reduces inference latency
by selectively postponing unlearning execution incurred by unlearning requests
from data owners, while strictly adhering to the RTBF principle. ERASER offers
three groups of design choices to allow for tailor-made variants that best suit
the specific environments and preferences of different MLaaS systems. Extensive
empirical evaluations across various settings confirm ERASER's effectiveness,
e.g., it can effectively save up to 99% of inference latency and 31% of
computation overhead over the inference-oblivion baseline. | Cryptography and Security |
What field is the article from? | Title: CarbNN: A Novel Active Transfer Learning Neural Network To Build De Novo Metal Organic Frameworks (MOFs) for Carbon Capture
Abstract: Over the past decade, climate change has become an increasing problem with
one of the major contributing factors being carbon dioxide (CO2) emissions;
almost 51% of total US carbon emissions are from factories. Current materials
used in CO2 capture are lacking either in efficiency, sustainability, or cost.
Electrocatalysis of CO2 is a new approach where CO2 can be reduced and the
components used industrially as fuel, saving transportation costs, creating
financial incentives. Metal Organic Frameworks (MOFs) are crystals made of
organo-metals that adsorb, filter, and electrocatalyze CO2. The current
available MOFs for capture & electrocatalysis are expensive to manufacture and
inefficient at capture. The goal therefore is to computationally design a MOF
that can adsorb CO2 and catalyze carbon monoxide & oxygen with low cost.
A novel active transfer learning neural network was developed, utilizing
transfer learning due to limited available data on 15 MOFs. Using the Cambridge
Structural Database with 10,000 MOFs, the model used incremental mutations to
fit a trained fitness hyper-heuristic function. Eventually, a Selenium MOF
(C18MgO25Se11Sn20Zn5) was converged on. Through analysis of predictions &
literature, the converged MOF was shown to be more effective & more
synthetically accessible than existing MOFs, showing the model had an
understanding of effective electrocatalytic structures in the material space.
This novel network can be implemented for other gas separations and catalysis
applications that have limited training accessible datasets. | Machine Learning |
What field is the article from? | Title: Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness?
Abstract: Neural language models (LMs) can be used to evaluate the truth of factual
statements in two ways: they can be either queried for statement probabilities,
or probed for internal representations of truthfulness. Past work has found
that these two procedures sometimes disagree, and that probes tend to be more
accurate than LM outputs. This has led some researchers to conclude that LMs
"lie" or otherwise encode non-cooperative communicative intents. Is this an
accurate description of today's LMs, or can query-probe disagreement arise in
other ways? We identify three different classes of disagreement, which we term
confabulation, deception, and heterogeneity. In many cases, the superiority of
probes is simply attributable to better calibration on uncertain answers rather
than a greater fraction of correct, high-confidence answers. In some cases,
queries and probes perform better on different subsets of inputs, and accuracy
can further be improved by ensembling the two. Code is available at
github.com/lingo-mit/lm-truthfulness. | Computational Linguistics |
What field is the article from? | Title: Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector
Abstract: Recent studies on semi-supervised learning (SSL) have achieved great success.
Despite their promising performance, current state-of-the-art methods tend
toward increasingly complex designs at the cost of introducing more network
components and additional training procedures. In this paper, we propose a
simple method named Ensemble Projectors Aided for Semi-supervised Learning
(EPASS), which focuses mainly on improving the learned embeddings to boost the
performance of the existing contrastive joint-training semi-supervised learning
frameworks. Unlike standard methods, where the learned embeddings from one
projector are stored in memory banks to be used with contrastive learning,
EPASS stores the ensemble embeddings from multiple projectors in memory banks.
As a result, EPASS improves generalization, strengthens feature representation,
and boosts performance. For instance, EPASS improves strong baselines for
semi-supervised learning by 39.47\%/31.39\%/24.70\% top-1 error rate, while
using only 100k/1\%/10\% of labeled data for SimMatch, and achieves
40.24\%/32.64\%/25.90\% top-1 error rate for CoMatch on the ImageNet dataset.
These improvements are consistent across methods, network architectures, and
datasets, proving the general effectiveness of the proposed methods. Code is
available at https://github.com/beandkay/EPASS. | Computer Vision |
What field is the article from? | Title: Meta learning with language models: Challenges and opportunities in the classification of imbalanced text
Abstract: Detecting out of policy speech (OOPS) content is important but difficult.
While machine learning is a powerful tool to tackle this challenging task, it
is hard to break the performance ceiling due to factors like quantity and
quality limitations on training data and inconsistencies in OOPS definition and
data labeling. To realize the full potential of available limited resources, we
propose a meta learning technique (MLT) that combines individual models built
with different text representations. We analytically show that the resulting
technique is numerically stable and produces reasonable combining weights. We
combine the MLT with a threshold-moving (TM) technique to further improve the
performance of the combined predictor on highly-imbalanced in-distribution and
out-of-distribution datasets. We also provide computational results to show the
statistically significant advantages of the proposed MLT approach.
All authors contributed equally to this work. | Machine Learning |
What field is the article from? | Title: Saturn: Efficient Multi-Large-Model Deep Learning
Abstract: In this paper, we propose Saturn, a new data system to improve the efficiency
of multi-large-model training (e.g., during model selection/hyperparameter
optimization). We first identify three key interconnected systems challenges
for users building large models in this setting -- parallelism technique
selection, distribution of GPUs over jobs, and scheduling. We then formalize
these as a joint problem, and build a new system architecture to tackle these
challenges simultaneously. Our evaluations show that our joint-optimization
approach yields 39-49% lower model selection runtimes than typical current DL
practice. | Machine Learning |
What field is the article from? | Title: Hessian Aware Low-Rank Weight Perturbation for Continual Learning
Abstract: Continual learning aims to learn a series of tasks sequentially without
forgetting the knowledge acquired from the previous ones. In this work, we
propose the Hessian Aware Low-Rank Perturbation algorithm for continual
learning. By modeling the parameter transitions along the sequential tasks with
the weight matrix transformation, we propose to apply the low-rank
approximation on the task-adaptive parameters in each layer of the neural
networks. Specifically, we theoretically demonstrate the quantitative
relationship between the Hessian and the proposed low-rank approximation. The
approximation ranks are then globally determined according to the marginal
increment of the empirical loss estimated by the layer-specific gradient and
low-rank approximation error. Furthermore, we control the model capacity by
pruning less important parameters to diminish the parameter growth. We conduct
extensive experiments on various benchmarks, including a dataset with
large-scale tasks, and compare our method against some recent state-of-the-art
methods to demonstrate the effectiveness and scalability of our proposed
method. Empirical results show that our method performs better on different
benchmarks, especially in achieving task order robustness and handling the
forgetting issue. A demo code can be found at https://github.com/lijiaqi/HALRP. | Machine Learning |
What field is the article from? | Title: Content-based Controls For Music Large Language Modeling
Abstract: Recent years have witnessed a rapid growth of large-scale language models in
the domain of music audio. Such models enable end-to-end generation of
higher-quality music, and some allow conditioned generation using text
descriptions. However, the control power of text controls on music is
intrinsically limited, as they can only describe music indirectly through
meta-data (such as singers and instruments) or high-level representations (such
as genre and emotion). We aim to further equip the models with direct and
content-based controls on innate music languages such as pitch, chords and drum
track. To this end, we contribute Coco-Mulla, a content-based control method
for music large language modeling. It uses a parameter-efficient fine-tuning
(PEFT) method tailored for Transformer-based audio models. Experiments show
that our approach achieved high-quality music generation with low-resource
semi-supervised learning, tuning with less than 4% parameters compared to the
original model and training on a small dataset with fewer than 300 songs.
Moreover, our approach enables effective content-based controls, and we
illustrate the control power via chords and rhythms, two of the most salient
features of music audio. Furthermore, we show that by combining content-based
controls and text descriptions, our system achieves flexible music variation
generation and style transfer. Our source codes and demos are available online. | Artificial Intelligence |
What field is the article from? | Title: Data-driven project planning: An integrated network learning and constraint relaxation approach in favor of scheduling
Abstract: Our focus is on projects, i.e., business processes, which are emerging as the
economic drivers of our times. Differently from day-to-day operational
processes that do not require detailed planning, a project requires planning
and resource-constrained scheduling for coordinating resources across sub- or
related projects and organizations. A planner in charge of project planning has
to select a set of activities to perform, determine their precedence
constraints, and schedule them according to temporal project constraints. We
suggest a data-driven project planning approach for classes of projects such as
infrastructure building and information systems development projects. A project
network is first learned from historical records. The discovered network
relaxes temporal constraints embedded in individual projects, thus uncovering
where planning and scheduling flexibility can be exploited for greater benefit.
Then, the network, which contains multiple project plan variations, from which
one has to be selected, is enriched by identifying decision rules and frequent
paths. The planner can rely on the project network for: 1) decoding a project
variation such that it forms a new project plan, and 2) applying
resource-constrained project scheduling procedures to determine the project's
schedule and resource allocation. Using two real-world project datasets, we
show that the suggested approach may provide the planner with significant
flexibility (up to a 26% reduction of the critical path of a real project) to
adjust the project plan and schedule. We believe that the proposed approach can
play an important part in supporting decision making towards automated
data-driven project planning. | Artificial Intelligence |
What field is the article from? | Title: Offline Imitation from Observation via Primal Wasserstein State Occupancy Matching
Abstract: In real-world scenarios, arbitrary interactions with the environment can
often be costly, and actions of expert demonstrations are not always available.
To reduce the need for both, Offline Learning from Observations (LfO) is
extensively studied, where the agent learns to solve a task with only expert
states and \textit{task-agnostic} non-expert state-action pairs. The
state-of-the-art DIstribution Correction Estimation (DICE) methods minimize the
state occupancy divergence between the learner and expert policies. However,
they are limited to either $f$-divergences (KL and $\chi^2$) or Wasserstein
distance with Rubinstein duality, the latter of which constrains the underlying
distance metric crucial to the performance of Wasserstein-based solutions. To
address this problem, we propose Primal Wasserstein DICE (PW-DICE), which
minimizes the primal Wasserstein distance between the expert and learner state
occupancies with a pessimistic regularizer and leverages a contrastively
learned distance as the underlying metric for the Wasserstein distance.
Theoretically, we prove that our framework is a generalization of the
state-of-the-art, SMODICE, and unifies $f$-divergence and Wasserstein
minimization. Empirically, we find that PW-DICE improves upon several
state-of-the-art methods on multiple testbeds. | Machine Learning |
What field is the article from? | Title: MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures
Abstract: The quest for accurate prediction of drug molecule properties poses a
fundamental challenge in the realm of Artificial Intelligence Drug Discovery
(AIDD). An effective representation of drug molecules emerges as a pivotal
component in this pursuit. Contemporary leading-edge research predominantly
resorts to self-supervised learning (SSL) techniques to extract meaningful
structural representations from large-scale, unlabeled molecular data,
subsequently fine-tuning these representations for an array of downstream
tasks. However, an inherent shortcoming of these studies lies in their singular
reliance on one modality of molecular information, such as molecule image or
SMILES representations, thus neglecting the potential complementarity of
various molecular modalities. In response to this limitation, we propose MolIG,
a novel MultiModaL molecular pre-training framework for predicting molecular
properties based on Image and Graph structures. MolIG model innovatively
leverages the coherence and correlation between molecule graph and molecule
image to execute self-supervised tasks, effectively amalgamating the strengths
of both molecular representation forms. This holistic approach allows for the
capture of pivotal molecular structural characteristics and high-level semantic
information. Upon completion of pre-training, Graph Neural Network (GNN)
Encoder is used for the prediction of downstream tasks. In comparison to
advanced baseline models, MolIG exhibits enhanced performance in downstream
tasks pertaining to molecular property prediction within benchmark groups such
as MoleculeNet Benchmark Group and ADMET Benchmark Group. | Machine Learning |
What field is the article from? | Title: Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models
Abstract: Document-level Relation Extraction (DocRE), which aims to extract relations
from a long context, is a critical challenge in achieving fine-grained
structural comprehension and generating interpretable document representations.
Inspired by recent advances in in-context learning capabilities emergent from
large language models (LLMs), such as ChatGPT, we aim to design an automated
annotation method for DocRE with minimum human effort. Unfortunately, vanilla
in-context learning is infeasible for document-level relation extraction due to
the plenty of predefined fine-grained relation types and the uncontrolled
generations of LLMs. To tackle this issue, we propose a method integrating a
large language model (LLM) and a natural language inference (NLI) module to
generate relation triples, thereby augmenting document-level relation datasets.
We demonstrate the effectiveness of our approach by introducing an enhanced
dataset known as DocGNRE, which excels in re-annotating numerous long-tail
relation types. We are confident that our method holds the potential for
broader applications in domain-specific relation type definitions and offers
tangible benefits in advancing generalized language semantic comprehension. | Computational Linguistics |
What field is the article from? | Title: Stellar: Systematic Evaluation of Human-Centric Personalized Text-to-Image Methods
Abstract: In this work, we systematically study the problem of personalized
text-to-image generation, where the output image is expected to portray
information about specific human subjects. E.g., generating images of oneself
appearing at imaginative places, interacting with various items, or engaging in
fictional activities. To this end, we focus on text-to-image systems that input
a single image of an individual to ground the generation process along with
text describing the desired visual context. Our first contribution is to fill
the literature gap by curating high-quality, appropriate data for this task.
Namely, we introduce a standardized dataset (Stellar) that contains
personalized prompts coupled with images of individuals that is an order of
magnitude larger than existing relevant datasets and where rich semantic
ground-truth annotations are readily available. Having established Stellar to
promote cross-systems fine-grained comparisons further, we introduce a rigorous
ensemble of specialized metrics that highlight and disentangle fundamental
properties such systems should obey. Besides being intuitive, our new metrics
correlate significantly more strongly with human judgment than currently used
metrics on this task. Last but not least, drawing inspiration from the recent
works of ELITE and SDXL, we derive a simple yet efficient, personalized
text-to-image baseline that does not require test-time fine-tuning for each
subject and which sets quantitatively and in human trials a new SoTA. For more
information, please visit our project's website:
https://stellar-gen-ai.github.io/. | Computer Vision |
What field is the article from? | Title: Temporal Shift -- Multi-Objective Loss Function for Improved Anomaly Fall Detection
Abstract: Falls are a major cause of injuries and deaths among older adults worldwide.
Accurate fall detection can help reduce potential injuries and additional
health complications. Different types of video modalities can be used in a home
setting to detect falls, including RGB, Infrared, and Thermal cameras. Anomaly
detection frameworks using autoencoders and their variants can be used for fall
detection due to the data imbalance that arises from the rarity and diversity
of falls. However, the use of reconstruction error in autoencoders can limit
the application of networks' structures that propagate information. In this
paper, we propose a new multi-objective loss function called Temporal Shift,
which aims to predict both future and reconstructed frames within a window of
sequential frames. The proposed loss function is evaluated on a
semi-naturalistic fall detection dataset containing multiple camera modalities.
The autoencoders were trained on normal activities of daily living (ADL)
performed by older adults and tested on ADLs and falls performed by young
adults. Temporal shift shows significant improvement to a baseline 3D
Convolutional autoencoder, an attention U-Net CAE, and a multi-modal neural
network. The greatest improvement was observed in an attention U-Net model
improving by 0.20 AUC ROC for a single camera when compared to reconstruction
alone. With significant improvement across different models, this approach has
the potential to be widely adopted and improve anomaly detection capabilities
in other settings besides fall detection. | Computer Vision |
What field is the article from? | Title: Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play
Abstract: Recent advances in Competitive Self-Play (CSP) have achieved, or even
surpassed, human level performance in complex game environments such as Dota 2
and StarCraft II using Distributed Multi-Agent Reinforcement Learning (MARL).
One core component of these methods relies on creating a pool of learning
agents -- consisting of the Main Agent, past versions of this agent, and
Exploiter Agents -- where Exploiter Agents learn counter-strategies to the Main
Agents. A key drawback of these approaches is the large computational cost and
physical time that is required to train the system, making them impractical to
deploy in highly iterative real-life settings such as video game productions.
In this paper, we propose the Minimax Exploiter, a game theoretic approach to
exploiting Main Agents that leverages knowledge of its opponents, leading to
significant increases in data efficiency. We validate our approach in a
diversity of settings, including simple turn based games, the arcade learning
environment, and For Honor, a modern video game. The Minimax Exploiter
consistently outperforms strong baselines, demonstrating improved stability and
data efficiency, leading to a robust CSP-MARL method that is both flexible and
easy to deploy. | Machine Learning |
What field is the article from? | Title: Automated Material Properties Extraction For Enhanced Beauty Product Discovery and Makeup Virtual Try-on
Abstract: The multitude of makeup products available can make it challenging to find
the ideal match for desired attributes. An intelligent approach for product
discovery is required to enhance the makeup shopping experience to make it more
convenient and satisfying. However, enabling accurate and efficient product
discovery requires extracting detailed attributes like color and finish type.
Our work introduces an automated pipeline that utilizes multiple customized
machine learning models to extract essential material attributes from makeup
product images. Our pipeline is versatile and capable of handling various
makeup products. To showcase the efficacy of our pipeline, we conduct extensive
experiments on eyeshadow products (both single and multi-shade ones), a
challenging makeup product known for its diverse range of shapes, colors, and
finish types. Furthermore, we demonstrate the applicability of our approach by
successfully extending it to other makeup categories like lipstick and
foundation, showcasing its adaptability and effectiveness across different
beauty products. Additionally, we conduct ablation experiments to demonstrate
the superiority of our machine learning pipeline over human labeling methods in
terms of reliability. Our proposed method showcases its effectiveness in
cross-category product discovery, specifically in recommending makeup products
that perfectly match a specified outfit. Lastly, we also demonstrate the
application of these material attributes in enabling virtual-try-on experiences
which makes makeup shopping experience significantly more engaging. | Computer Vision |
What field is the article from? | Title: Investigating Data Contamination in Modern Benchmarks for Large Language Models
Abstract: Recent observations have underscored a disparity between the inflated
benchmark scores and the actual performance of LLMs, raising concerns about
potential contamination of evaluation benchmarks. This issue is especially
critical for closed-source models and certain open-source models where training
data transparency is lacking. In this paper we study data contamination by
proposing two methods tailored for both open-source and proprietary LLMs. We
first introduce a retrieval-based system to explore potential overlaps between
evaluation benchmarks and pretraining corpora. We further present a novel
investigation protocol named \textbf{T}estset \textbf{S}lot Guessing
(\textit{TS-Guessing}), applicable to both open and proprietary models. This
approach entails masking a wrong answer in a multiple-choice question and
prompting the model to fill in the gap. Additionally, it involves obscuring an
unlikely word in an evaluation example and asking the model to produce it. We
find that certain commercial LLMs could surprisingly guess the missing option
in various test sets. Specifically, in the TruthfulQA benchmark, we find that
LLMs exhibit notable performance improvement when provided with additional
metadata in the benchmark. Further, in the MMLU benchmark, ChatGPT and GPT-4
demonstrated an exact match rate of 52\% and 57\%, respectively, in guessing
the missing options in benchmark test data. We hope these results underscore
the need for more robust evaluation methodologies and benchmarks in the field. | Computational Linguistics |
What field is the article from? | Title: Modular Neural Networks for Time Series Forecasting: Interpretability and Feature Selection using Attention
Abstract: Multivariate time series have many applications, from healthcare and
meteorology to life science. Although deep learning models have shown excellent
predictive performance for time series, they have been criticised for being
"black-boxes" or non-interpretable. This paper proposes a novel modular neural
network model for multivariate time series prediction that is interpretable by
construction. A recurrent neural network learns the temporal dependencies in
the data while an attention-based feature selection component selects the most
relevant features and suppresses redundant features used in the learning of the
temporal dependencies. A modular deep network is trained from the selected
features independently to show the users how features influence outcomes,
making the model interpretable. Experimental results show that this approach
can outperform state-of-the-art interpretable Neural Additive Models (NAM) and
variations thereof in both regression and classification of time series tasks,
achieving a predictive performance that is comparable to the top
non-interpretable methods for time series, LSTM and XGBoost. | Machine Learning |
What field is the article from? | Title: Autonomous Port Navigation With Ranging Sensors Using Model-Based Reinforcement Learning
Abstract: Autonomous shipping has recently gained much interest in the research
community. However, little research focuses on inland - and port navigation,
even though this is identified by countries such as Belgium and the Netherlands
as an essential step towards a sustainable future. These environments pose
unique challenges, since they can contain dynamic obstacles that do not
broadcast their location, such as small vessels, kayaks or buoys. Therefore,
this research proposes a navigational algorithm which can navigate an inland
vessel in a wide variety of complex port scenarios using ranging sensors to
observe the environment. The proposed methodology is based on a machine
learning approach that has recently set benchmark results in various domains:
model-based reinforcement learning. By randomizing the port environments during
training, the trained model can navigate in scenarios that it never encountered
during training. Furthermore, results show that our approach outperforms the
commonly used dynamic window approach and a benchmark model-free reinforcement
learning algorithm. This work is therefore a significant step towards vessels
that can navigate autonomously in complex port scenarios. | Robotics |
What field is the article from? | Title: Salespeople vs SalesBot: Exploring the Role of Educational Value in Conversational Recommender Systems
Abstract: Making big purchases requires consumers to research or consult a salesperson
to gain domain expertise. However, existing conversational recommender systems
(CRS) often overlook users' lack of background knowledge, focusing solely on
gathering preferences. In this work, we define a new problem space for
conversational agents that aim to provide both product recommendations and
educational value through mixed-type mixed-initiative dialog. We introduce
SalesOps, a framework that facilitates the simulation and evaluation of such
systems by leveraging recent advancements in large language models (LLMs). We
build SalesBot and ShopperBot, a pair of LLM-powered agents that can simulate
either side of the framework. A comprehensive human study compares SalesBot
against professional salespeople, revealing that although SalesBot approaches
professional performance in terms of fluency and informativeness, it lags
behind in recommendation quality. We emphasize the distinct limitations both
face in providing truthful information, highlighting the challenges of ensuring
faithfulness in the CRS context. We release our code and make all data
available. | Computational Linguistics |
What field is the article from? | Title: Look-Ahead Selective Plasticity for Continual Learning of Visual Tasks
Abstract: Contrastive representation learning has emerged as a promising technique for
continual learning as it can learn representations that are robust to
catastrophic forgetting and generalize well to unseen future tasks. Previous
work in continual learning has addressed forgetting by using previous task data
and trained models. Inspired by event models created and updated in the brain,
we propose a new mechanism that takes place during task boundaries, i.e., when
one task finishes and another starts. By observing the redundancy-inducing
ability of contrastive loss on the output of a neural network, our method
leverages the first few samples of the new task to identify and retain
parameters contributing most to the transfer ability of the neural network,
freeing up the remaining parts of the network to learn new features. We
evaluate the proposed methods on benchmark computer vision datasets including
CIFAR10 and TinyImagenet and demonstrate state-of-the-art performance in the
task-incremental, class-incremental, and domain-incremental continual learning
scenarios. | Computer Vision |
What field is the article from? | Title: Towards Efficient 3D Object Detection in Bird's-Eye-View Space for Autonomous Driving: A Convolutional-Only Approach
Abstract: 3D object detection in Bird's-Eye-View (BEV) space has recently emerged as a
prevalent approach in the field of autonomous driving. Despite the demonstrated
improvements in accuracy and velocity estimation compared to perspective view
methods, the deployment of BEV-based techniques in real-world autonomous
vehicles remains challenging. This is primarily due to their reliance on
vision-transformer (ViT) based architectures, which introduce quadratic
complexity with respect to the input resolution. To address this issue, we
propose an efficient BEV-based 3D detection framework called BEVENet, which
leverages a convolutional-only architectural design to circumvent the
limitations of ViT models while maintaining the effectiveness of BEV-based
methods. Our experiments show that BEVENet is 3$\times$ faster than
contemporary state-of-the-art (SOTA) approaches on the NuScenes challenge,
achieving a mean average precision (mAP) of 0.456 and a nuScenes detection
score (NDS) of 0.555 on the NuScenes validation dataset, with an inference
speed of 47.6 frames per second. To the best of our knowledge, this study
stands as the first to achieve such significant efficiency improvements for
BEV-based methods, highlighting their enhanced feasibility for real-world
autonomous driving applications. | Computer Vision |
What field is the article from? | Title: Can ChatGPT advance software testing intelligence? An experience report on metamorphic testing
Abstract: While ChatGPT is a well-known artificial intelligence chatbot being used to
answer human's questions, one may want to discover its potential in advancing
software testing. We examine the capability of ChatGPT in advancing the
intelligence of software testing through a case study on metamorphic testing
(MT), a state-of-the-art software testing technique. We ask ChatGPT to generate
candidates of metamorphic relations (MRs), which are basically necessary
properties of the object program and which traditionally require human
intelligence to identify. These MR candidates are then evaluated in terms of
correctness by domain experts. We show that ChatGPT can be used to generate new
correct MRs to test several software systems. Having said that, the majority of
MR candidates are either defined vaguely or incorrect, especially for systems
that have never been tested with MT. ChatGPT can be used to advance software
testing intelligence by proposing MR candidates that can be later adopted for
implementing tests; but human intelligence should still inevitably be involved
to justify and rectify their correctness. | Software Engineering |
What field is the article from? | Title: An Interactive Query Generation Assistant using LLM-based Prompt Modification and User Feedback
Abstract: While search is the predominant method of accessing information, formulating
effective queries remains a challenging task, especially for situations where
the users are not familiar with a domain, or searching for documents in other
languages, or looking for complex information such as events, which are not
easily expressible as queries. Providing example documents or passages of
interest, might be easier for a user, however, such query-by-example scenarios
are prone to concept drift, and are highly sensitive to the query generation
method. This demo illustrates complementary approaches of using LLMs
interactively, assisting and enabling the user to provide edits and feedback at
all stages of the query formulation process. The proposed Query Generation
Assistant is a novel search interface which supports automatic and interactive
query generation over a mono-linguial or multi-lingual document collection.
Specifically, the proposed assistive interface enables the users to refine the
queries generated by different LLMs, to provide feedback on the retrieved
documents or passages, and is able to incorporate the users' feedback as
prompts to generate more effective queries. The proposed interface is a
valuable experimental tool for exploring fine-tuning and prompting of LLMs for
query generation to qualitatively evaluate the effectiveness of retrieval and
ranking models, and for conducting Human-in-the-Loop (HITL) experiments for
complex search tasks where users struggle to formulate queries without such
assistance. | Artificial Intelligence |
What field is the article from? | Title: SAT-Based Algorithms for Regular Graph Pattern Matching
Abstract: Graph matching is a fundamental problem in pattern recognition, with many
applications such as software analysis and computational biology. One
well-known type of graph matching problem is graph isomorphism, which consists
of deciding if two graphs are identical. Despite its usefulness, the properties
that one may check using graph isomorphism are rather limited, since it only
allows strict equality checks between two graphs. For example, it does not
allow one to check complex structural properties such as if the target graph is
an arbitrary length sequence followed by an arbitrary size loop.
We propose a generalization of graph isomorphism that allows one to check
such properties through a declarative specification. This specification is
given in the form of a Regular Graph Pattern (ReGaP), a special type of graph,
inspired by regular expressions, that may contain wildcard nodes that represent
arbitrary structures such as variable-sized sequences or subgraphs. We propose
a SAT-based algorithm for checking if a target graph matches a given ReGaP. We
also propose a preprocessing technique for improving the performance of the
algorithm and evaluate it through an extensive experimental evaluation on
benchmarks from the CodeSearchNet dataset. | Artificial Intelligence |
What field is the article from? | Title: LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking
Abstract: Recently, large language models (LLMs) have exhibited significant progress in
language understanding and generation. By leveraging textual features,
customized LLMs are also applied for recommendation and demonstrate
improvements across diverse recommendation scenarios. Yet the majority of
existing methods perform training-free recommendation that heavily relies on
pretrained knowledge (e.g., movie recommendation). In addition, inference on
LLMs is slow due to autoregressive generation, rendering existing methods less
effective for real-time recommendation. As such, we propose a two-stage
framework using large language models for ranking-based recommendation
(LlamaRec). In particular, we use small-scale sequential recommenders to
retrieve candidates based on the user interaction history. Then, both history
and retrieved items are fed to the LLM in text via a carefully designed prompt
template. Instead of generating next-item titles, we adopt a verbalizer-based
approach that transforms output logits into probability distributions over the
candidate items. Therefore, the proposed LlamaRec can efficiently rank items
without generating long text. To validate the effectiveness of the proposed
framework, we compare against state-of-the-art baseline methods on benchmark
datasets. Our experimental results demonstrate the performance of LlamaRec,
which consistently achieves superior performance in both recommendation
performance and efficiency. | Information Retrieval |
What field is the article from? | Title: TSegFormer: 3D Tooth Segmentation in Intraoral Scans with Geometry Guided Transformer
Abstract: Optical Intraoral Scanners (IOS) are widely used in digital dentistry to
provide detailed 3D information of dental crowns and the gingiva. Accurate 3D
tooth segmentation in IOSs is critical for various dental applications, while
previous methods are error-prone at complicated boundaries and exhibit
unsatisfactory results across patients. In this paper, we propose TSegFormer
which captures both local and global dependencies among different teeth and the
gingiva in the IOS point clouds with a multi-task 3D transformer architecture.
Moreover, we design a geometry-guided loss based on a novel point curvature to
refine boundaries in an end-to-end manner, avoiding time-consuming
post-processing to reach clinically applicable segmentation. In addition, we
create a dataset with 16,000 IOSs, the largest ever IOS dataset to the best of
our knowledge. The experimental results demonstrate that our TSegFormer
consistently surpasses existing state-of-the-art baselines. The superiority of
TSegFormer is corroborated by extensive analysis, visualizations and real-world
clinical applicability tests. Our code is available at
https://github.com/huiminxiong/TSegFormer. | Computer Vision |
What field is the article from? | Title: VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Models
Abstract: The ability to perceive how objects change over time is a crucial ingredient
in human intelligence. However, current benchmarks cannot faithfully reflect
the temporal understanding abilities of video-language models (VidLMs) due to
the existence of static visual shortcuts. To remedy this issue, we present
VITATECS, a diagnostic VIdeo-Text dAtaset for the evaluation of TEmporal
Concept underStanding. Specifically, we first introduce a fine-grained taxonomy
of temporal concepts in natural language in order to diagnose the capability of
VidLMs to comprehend different temporal aspects. Furthermore, to disentangle
the correlation between static and temporal information, we generate
counterfactual video descriptions that differ from the original one only in the
specified temporal aspect. We employ a semi-automatic data collection framework
using large language models and human-in-the-loop annotation to obtain
high-quality counterfactual descriptions efficiently. Evaluation of
representative video-language understanding models confirms their deficiency in
temporal understanding, revealing the need for greater emphasis on the temporal
elements in video-language research. | Computer Vision |
What field is the article from? | Title: Minimizing Factual Inconsistency and Hallucination in Large Language Models
Abstract: Large Language Models (LLMs) are widely used in critical fields such as
healthcare, education, and finance due to their remarkable proficiency in
various language-related tasks. However, LLMs are prone to generating factually
incorrect responses or "hallucinations," which can lead to a loss of
credibility and trust among users. To address this issue, we propose a
multi-stage framework that generates the rationale first, verifies and refines
incorrect ones, and uses them as supporting references to generate the answer.
The generated rationale enhances the transparency of the answer and our
framework provides insights into how the model arrived at this answer, by using
this rationale and the references to the context. In this paper, we demonstrate
its effectiveness in improving the quality of responses to drug-related
inquiries in the life sciences industry. Our framework improves traditional
Retrieval Augmented Generation (RAG) by enabling OpenAI GPT-3.5-turbo to be
14-25% more faithful and 16-22% more accurate on two datasets. Furthermore,
fine-tuning samples based on our framework improves the accuracy of smaller
open-access LLMs by 33-42% and competes with RAG on commercial models. | Computational Linguistics |
What field is the article from? | Title: TaCo: Enhancing Cross-Lingual Transfer for Low-Resource Languages in LLMs through Translation-Assisted Chain-of-Thought Processes
Abstract: LLMs such as ChatGPT and PaLM can be utilized to train on a new language and
revitalize low-resource languages. However, it is evidently very costly to
pretrain pr fine-tune LLMs to adopt new languages. Another challenge is the
limitation of benchmark datasets and the metrics used to measure the
performance of models in multilingual settings. This paper proposes
cost-effective solutions to both of the aforementioned challenges. We introduce
the Multilingual Instruction-Tuning Dataset (MITS), which is comprised of the
translation of Alpaca-52K, Dolly-15K, and Vicuna Benchmark in 132 languages.
Also, we propose a new method called \emph{TaCo: Translation-Assisted
Cross-Linguality}, which make uses of translation in a chain-of-thought process
to instruction-tune LLMs on a new languages through a curriculum learning
process. As a proof of concept, we experimented with the instruction-tuned
Guanaco-33B model and performed further instruction tuning using the TaCo
method in three low-resource languages and one high-resource language. Our
results show that the TaCo method impresses the GPT-4 with 82% for a
low-resource language in the Vicuna Benchmark dataset, and boosts performance
by double in contrast to the performance of instruction tuning only. Our
results show that TaCo is a promising method for creating multilingual LLMs,
even for low-resource languages. We have released our datasets and the model
adapters, and encourage the research community to make use of these resources
towards advancing work on multilingual LLMs. | Computational Linguistics |
What field is the article from? | Title: Navigating the generative AI era: Introducing the AI assessment scale for ethical GenAI assessment
Abstract: Recent developments in Generative Artificial Intelligence (GenAI) have
created a paradigm shift in multiple areas of society, and the use of these
technologies is likely to become a defining feature of education in coming
decades. GenAI offers transformative pedagogical opportunities, while
simultaneously posing ethical and academic challenges. Against this backdrop,
we outline a practical, simple, and sufficiently comprehensive tool to allow
for the integration of GenAI tools into educational assessment: the AI
Assessment Scale (AIAS). The AIAS empowers educators to select the appropriate
level of GenAI usage in assessments based on the learning outcomes they seek to
address. The AIAS offers greater clarity and transparency for students and
educators, provides a fair and equitable policy tool for institutions to work
with, and offers a nuanced approach which embraces the opportunities of GenAI
while recognising that there are instances where such tools may not be
pedagogically appropriate or necessary. By adopting a practical, flexible
approach that can be implemented quickly, the AIAS can form a much-needed
starting point to address the current uncertainty and anxiety regarding GenAI
in education. As a secondary objective, we engage with the current literature
and advocate for a refocused discourse on GenAI tools in education, one which
foregrounds how technologies can help support and enhance teaching and
learning, which contrasts with the current focus on GenAI as a facilitator of
academic misconduct. | Artificial Intelligence |
What field is the article from? | Title: Gaussian Grouping: Segment and Edit Anything in 3D Scenes
Abstract: The recent Gaussian Splatting achieves high-quality and real-time novel-view
synthesis of the 3D scenes. However, it is solely concentrated on the
appearance and geometry modeling, while lacking in fine-grained object-level
scene understanding. To address this issue, we propose Gaussian Grouping, which
extends Gaussian Splatting to jointly reconstruct and segment anything in
open-world 3D scenes. We augment each Gaussian with a compact Identity
Encoding, allowing the Gaussians to be grouped according to their object
instance or stuff membership in the 3D scene. Instead of resorting to expensive
3D labels, we supervise the Identity Encodings during the differentiable
rendering by leveraging the 2D mask predictions by SAM, along with introduced
3D spatial consistency regularization. Comparing to the implicit NeRF
representation, we show that the discrete and grouped 3D Gaussians can
reconstruct, segment and edit anything in 3D with high visual quality, fine
granularity and efficiency. Based on Gaussian Grouping, we further propose a
local Gaussian Editing scheme, which shows efficacy in versatile scene editing
applications, including 3D object removal, inpainting, colorization and scene
recomposition. Our code and models will be at
https://github.com/lkeab/gaussian-grouping. | Computer Vision |
What field is the article from? | Title: TempTabQA: Temporal Question Answering for Semi-Structured Tables
Abstract: Semi-structured data, such as Infobox tables, often include temporal
information about entities, either implicitly or explicitly. Can current NLP
systems reason about such information in semi-structured tables? To tackle this
question, we introduce the task of temporal question answering on
semi-structured tables. We present a dataset, TempTabQA, which comprises 11,454
question-answer pairs extracted from 1,208 Wikipedia Infobox tables spanning
more than 90 distinct domains. Using this dataset, we evaluate several
state-of-the-art models for temporal reasoning. We observe that even the
top-performing LLMs lag behind human performance by more than 13.5 F1 points.
Given these results, our dataset has the potential to serve as a challenging
benchmark to improve the temporal reasoning capabilities of NLP models. | Computational Linguistics |
What field is the article from? | Title: The Significance of Machine Learning in Clinical Disease Diagnosis: A Review
Abstract: The global need for effective disease diagnosis remains substantial, given
the complexities of various disease mechanisms and diverse patient symptoms. To
tackle these challenges, researchers, physicians, and patients are turning to
machine learning (ML), an artificial intelligence (AI) discipline, to develop
solutions. By leveraging sophisticated ML and AI methods, healthcare
stakeholders gain enhanced diagnostic and treatment capabilities. However,
there is a scarcity of research focused on ML algorithms for enhancing the
accuracy and computational efficiency. This research investigates the capacity
of machine learning algorithms to improve the transmission of heart rate data
in time series healthcare metrics, concentrating particularly on optimizing
accuracy and efficiency. By exploring various ML algorithms used in healthcare
applications, the review presents the latest trends and approaches in ML-based
disease diagnosis (MLBDD). The factors under consideration include the
algorithm utilized, the types of diseases targeted, the data types employed,
the applications, and the evaluation metrics. This review aims to shed light on
the prospects of ML in healthcare, particularly in disease diagnosis. By
analyzing the current literature, the study provides insights into
state-of-the-art methodologies and their performance metrics. | Machine Learning |
What field is the article from? | Title: Get the Ball Rolling: Alerting Autonomous Robots When to Help to Close the Healthcare Loop
Abstract: To facilitate the advancement of research in healthcare robots without human
intervention or commands, we introduce the Autonomous Helping Challenge, along
with a crowd-sourcing large-scale dataset. The goal is to create healthcare
robots that possess the ability to determine when assistance is necessary,
generate useful sub-tasks to aid in planning, carry out these plans through a
physical robot, and receive feedback from the environment in order to generate
new tasks and continue the process. Besides the general challenge in open-ended
scenarios, Autonomous Helping focuses on three specific challenges: autonomous
task generation, the gap between the current scene and static commonsense, and
the gap between language instruction and the real world. Additionally, we
propose Helpy, a potential approach to close the healthcare loop in the
learning-free setting. | Robotics |
What field is the article from? | Title: EMDM: Efficient Motion Diffusion Model for Fast, High-Quality Motion Generation
Abstract: We introduce Efficient Motion Diffusion Model (EMDM) for fast and
high-quality human motion generation. Although previous motion diffusion models
have shown impressive results, they struggle to achieve fast generation while
maintaining high-quality human motions. Motion latent diffusion has been
proposed for efficient motion generation. However, effectively learning a
latent space can be non-trivial in such a two-stage manner. Meanwhile,
accelerating motion sampling by increasing the step size, e.g., DDIM, typically
leads to a decline in motion quality due to the inapproximation of complex data
distributions when naively increasing the step size. In this paper, we propose
EMDM that allows for much fewer sample steps for fast motion generation by
modeling the complex denoising distribution during multiple sampling steps.
Specifically, we develop a Conditional Denoising Diffusion GAN to capture
multimodal data distributions conditioned on both control signals, i.e.,
textual description and denoising time step. By modeling the complex data
distribution, a larger sampling step size and fewer steps are achieved during
motion synthesis, significantly accelerating the generation process. To
effectively capture the human dynamics and reduce undesired artifacts, we
employ motion geometric loss during network training, which improves the motion
quality and training efficiency. As a result, EMDM achieves a remarkable
speed-up at the generation stage while maintaining high-quality motion
generation in terms of fidelity and diversity. | Computer Vision |
What field is the article from? | Title: Dance of Channel and Sequence: An Efficient Attention-Based Approach for Multivariate Time Series Forecasting
Abstract: In recent developments, predictive models for multivariate time series
analysis have exhibited commendable performance through the adoption of the
prevalent principle of channel independence. Nevertheless, it is imperative to
acknowledge the intricate interplay among channels, which fundamentally
influences the outcomes of multivariate predictions. Consequently, the notion
of channel independence, while offering utility to a certain extent, becomes
increasingly impractical, leading to information degradation. In response to
this pressing concern, we present CSformer, an innovative framework
characterized by a meticulously engineered two-stage self-attention mechanism.
This mechanism is purposefully designed to enable the segregated extraction of
sequence-specific and channel-specific information, while sharing parameters to
promote synergy and mutual reinforcement between sequences and channels.
Simultaneously, we introduce sequence adapters and channel adapters, ensuring
the model's ability to discern salient features across various dimensions.
Rigorous experimentation, spanning multiple real-world datasets, underscores
the robustness of our approach, consistently establishing its position at the
forefront of predictive performance across all datasets. This augmentation
substantially enhances the capacity for feature extraction inherent to
multivariate time series data, facilitating a more comprehensive exploitation
of the available information. | Machine Learning |
What field is the article from? | Title: Auto MC-Reward: Automated Dense Reward Design with Large Language Models for Minecraft
Abstract: Traditional reinforcement-learning-based agents rely on sparse rewards that
often only use binary values to indicate task completion or failure. The
challenge in exploration efficiency makes it difficult to effectively learn
complex tasks in Minecraft. To address this, this paper introduces an advanced
learning system, named Auto MC-Reward, that leverages Large Language Models
(LLMs) to automatically design dense reward functions, thereby enhancing the
learning efficiency. Auto MC-Reward consists of three important components:
Reward Designer, Reward Critic, and Trajectory Analyzer. Given the environment
information and task descriptions, the Reward Designer first design the reward
function by coding an executable Python function with predefined observation
inputs. Then, our Reward Critic will be responsible for verifying the code,
checking whether the code is self-consistent and free of syntax and semantic
errors. Further, the Trajectory Analyzer summarizes possible failure causes and
provides refinement suggestions according to collected trajectories. In the
next round, Reward Designer will take further refine and iterate the dense
reward function based on feedback. Experiments demonstrate a significant
improvement in the success rate and learning efficiency of our agents in
complex tasks in Minecraft, such as obtaining diamond with the efficient
ability to avoid lava, and efficiently explore trees and animals that are
sparse on the plains biome. | Artificial Intelligence |
What field is the article from? | Title: Data Factors for Better Compositional Generalization
Abstract: Recent diagnostic datasets on compositional generalization, such as SCAN
(Lake and Baroni, 2018) and COGS (Kim and Linzen, 2020), expose severe problems
in models trained from scratch on these datasets. However, in contrast to this
poor performance, state-of-the-art models trained on larger and more general
datasets show better generalization ability. In this work, to reconcile this
inconsistency, we conduct an empirical analysis by training Transformer models
on a variety of training sets with different data factors, including dataset
scale, pattern complexity, example difficulty, etc. First, we show that
increased dataset complexity can lead to better generalization behavior on
multiple different generalization challenges. To further understand this
improvement, we show two axes of the benefit from more complex datasets: they
provide more diverse examples so compositional understanding becomes more
effective, and they also prevent ungeneralizable memorization of the examples
due to reduced example repetition frequency. Finally, we explore how training
examples of different difficulty levels influence generalization differently.
On synthetic datasets, simple examples invoke stronger compositionality than
hard examples do. On larger-scale real language datasets, while hard examples
become more important potentially to ensure decent data coverage, a balanced
mixture of simple and hard examples manages to induce the strongest
generalizability. The code and data for this work are available at
https://github.com/owenzx/data4comp | Computational Linguistics |
What field is the article from? | Title: Efficient Representation of the Activation Space in Deep Neural Networks
Abstract: The representations of the activation space of deep neural networks (DNNs)
are widely utilized for tasks like natural language processing, anomaly
detection and speech recognition. Due to the diverse nature of these tasks and
the large size of DNNs, an efficient and task-independent representation of
activations becomes crucial. Empirical p-values have been used to quantify the
relative strength of an observed node activation compared to activations
created by already-known inputs. Nonetheless, keeping raw data for these
calculations increases memory resource consumption and raises privacy concerns.
To this end, we propose a model-agnostic framework for creating representations
of activations in DNNs using node-specific histograms to compute p-values of
observed activations without retaining already-known inputs. Our proposed
approach demonstrates promising potential when validated with multiple network
architectures across various downstream tasks and compared with the kernel
density estimates and brute-force empirical baselines. In addition, the
framework reduces memory usage by 30% with up to 4 times faster p-value
computing time while maintaining state of-the-art detection power in downstream
tasks such as the detection of adversarial attacks and synthesized content.
Moreover, as we do not persist raw data at inference time, we could potentially
reduce susceptibility to attacks and privacy issues. | Machine Learning |
What field is the article from? | Title: Few-shot Hybrid Domain Adaptation of Image Generators
Abstract: Can a pre-trained generator be adapted to the hybrid of multiple target
domains and generate images with integrated attributes of them? In this work,
we introduce a new task -- Few-shot Hybrid Domain Adaptation (HDA). Given a
source generator and several target domains, HDA aims to acquire an adapted
generator that preserves the integrated attributes of all target domains,
without overriding the source domain's characteristics. Compared with Domain
Adaptation (DA), HDA offers greater flexibility and versatility to adapt
generators to more composite and expansive domains. Simultaneously, HDA also
presents more challenges than DA as we have access only to images from
individual target domains and lack authentic images from the hybrid domain. To
address this issue, we introduce a discriminator-free framework that directly
encodes different domains' images into well-separable subspaces. To achieve
HDA, we propose a novel directional subspace loss comprised of a distance loss
and a direction loss. Concretely, the distance loss blends the attributes of
all target domains by reducing the distances from generated images to all
target subspaces. The direction loss preserves the characteristics from the
source domain by guiding the adaptation along the perpendicular to subspaces.
Experiments show that our method can obtain numerous domain-specific attributes
in a single adapted generator, which surpasses the baseline methods in semantic
similarity, image fidelity, and cross-domain consistency. | Computer Vision |
What field is the article from? | Title: USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery
Abstract: Large, self-supervised vision models have led to substantial advancements for
automatically interpreting natural images. Recent works have begun tailoring
these methods to remote sensing data which has rich structure with
multi-sensor, multi-spectral, and temporal information providing massive
amounts of self-labeled data that can be used for self-supervised pre-training.
In this work, we develop a new encoder architecture called USat that can input
multi-spectral data from multiple sensors for self-supervised pre-training.
USat is a vision transformer with modified patch projection layers and
positional encodings to model spectral bands with varying spatial scales from
multiple sensors. We integrate USat into a Masked Autoencoder (MAE)
self-supervised pre-training procedure and find that a pre-trained USat
outperforms state-of-the-art self-supervised MAE models trained on remote
sensing data on multiple remote sensing benchmark datasets (up to 8%) and leads
to improvements in low data regimes (up to 7%). Code and pre-trained weights
are available at https://github.com/stanfordmlgroup/USat . | Computer Vision |
What field is the article from? | Title: TriDeNT: Triple Deep Network Training for Privileged Knowledge Distillation in Histopathology
Abstract: Computational pathology models rarely utilise data that will not be available
for inference. This means most models cannot learn from highly informative data
such as additional immunohistochemical (IHC) stains and spatial
transcriptomics. We present TriDeNT, a novel self-supervised method for
utilising privileged data that is not available during inference to improve
performance. We demonstrate the efficacy of this method for a range of
different paired data including immunohistochemistry, spatial transcriptomics
and expert nuclei annotations. In all settings, TriDeNT outperforms other
state-of-the-art methods in downstream tasks, with observed improvements of up
to 101%. Furthermore, we provide qualitative and quantitative measurements of
the features learned by these models and how they differ from baselines.
TriDeNT offers a novel method to distil knowledge from scarce or costly data
during training, to create significantly better models for routine inputs. | Computer Vision |
What field is the article from? | Title: Cross-domain feature disentanglement for interpretable modeling of tumor microenvironment impact on drug response
Abstract: High-throughput screening technology has facilitated the generation of
large-scale drug responses across hundreds of cancer cell lines. However, there
exists significant discrepancy between in vitro cell lines and actual tumors in
vivo in terms of their response to drug treatments, because of tumors comprise
of complex cellular compositions and histopathology structure, known as tumor
microenvironment (TME), which greatly influences the drug cytotoxicity against
tumor cells. To date, no study has focused on modeling the impact of the TME on
clinical drug response. This paper proposed a domain adaptation network for
feature disentanglement to separate representations of cancer cells and TME of
a tumor in patients. Two denoising autoencoders were separately used to extract
features from cell lines (source domain) and tumors (target domain) for partial
domain alignment and feature decoupling. The specific encoder was enforced to
extract information only about TME. Moreover, to ensure generalizability to
novel drugs, we applied a graph attention network to learn the latent
representation of drugs, allowing us to linearly model the drug perturbation on
cellular state in latent space. We calibrated our model on a benchmark dataset
and demonstrated its superior performance in predicting clinical drug response
and dissecting the influence of the TME on drug efficacy. | Machine Learning |
What field is the article from? | Title: Assessing Prompt Injection Risks in 200+ Custom GPTs
Abstract: In the rapidly evolving landscape of artificial intelligence, ChatGPT has
been widely used in various applications. The new feature: customization of
ChatGPT models by users to cater to specific needs has opened new frontiers in
AI utility. However, this study reveals a significant security vulnerability
inherent in these user-customized GPTs: prompt injection attacks. Through
comprehensive testing of over 200 user-designed GPT models via adversarial
prompts, we demonstrate that these systems are susceptible to prompt
injections. Through prompt injection, an adversary can not only extract the
customized system prompts but also access the uploaded files. This paper
provides a first-hand analysis of the prompt injection, alongside the
evaluation of the possible mitigation of such attacks. Our findings underscore
the urgent need for robust security frameworks in the design and deployment of
customizable GPT models. The intent of this paper is to raise awareness and
prompt action in the AI community, ensuring that the benefits of GPT
customization do not come at the cost of compromised security and privacy. | Cryptography and Security |
What field is the article from? | Title: Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding
Abstract: Current Large Language Models (LLMs) are unparalleled in their ability to
generate grammatically correct, fluent text. LLMs are appearing rapidly, and
debates on LLM capacities have taken off, but reflection is lagging behind.
Thus, in this position paper, we first zoom in on the debate and critically
assess three points recurring in critiques of LLM capacities: i) that LLMs only
parrot statistical patterns in the training data; ii) that LLMs master formal
but not functional language competence; and iii) that language learning in LLMs
cannot inform human language learning. Drawing on empirical and theoretical
arguments, we show that these points need more nuance. Second, we outline a
pragmatic perspective on the issue of `real' understanding and intentionality
in LLMs. Understanding and intentionality pertain to unobservable mental states
we attribute to other humans because they have pragmatic value: they allow us
to abstract away from complex underlying mechanics and predict behaviour
effectively. We reflect on the circumstances under which it would make sense
for humans to similarly attribute mental states to LLMs, thereby outlining a
pragmatic philosophical context for LLMs as an increasingly prominent
technology in society. | Computational Linguistics |
What field is the article from? | Title: ReCoRe: Regularized Contrastive Representation Learning of World Model
Abstract: While recent model-free Reinforcement Learning (RL) methods have demonstrated
human-level effectiveness in gaming environments, their success in everyday
tasks like visual navigation has been limited, particularly under significant
appearance variations. This limitation arises from (i) poor sample efficiency
and (ii) over-fitting to training scenarios. To address these challenges, we
present a world model that learns invariant features using (i) contrastive
unsupervised learning and (ii) an intervention-invariant regularizer. Learning
an explicit representation of the world dynamics i.e. a world model, improves
sample efficiency while contrastive learning implicitly enforces learning of
invariant features, which improves generalization. However, the naive
integration of contrastive loss to world models fails due to a lack of
supervisory signals to the visual encoder, as world-model-based RL methods
independently optimize representation learning and agent policy. To overcome
this issue, we propose an intervention-invariant regularizer in the form of an
auxiliary task such as depth prediction, image denoising, etc., that explicitly
enforces invariance to style-interventions. Our method outperforms current
state-of-the-art model-based and model-free RL methods and significantly on
out-of-distribution point navigation task evaluated on the iGibson benchmark.
We further demonstrate that our approach, with only visual observations,
outperforms recent language-guided foundation models for point navigation,
which is essential for deployment on robots with limited computation
capabilities. Finally, we demonstrate that our proposed model excels at the
sim-to-real transfer of its perception module on Gibson benchmark. | Machine Learning |
What field is the article from? | Title: Preserving the knowledge of long clinical texts using aggregated ensembles of large language models
Abstract: Clinical texts, such as admission notes, discharge summaries, and progress
notes, contain rich and valuable information that can be used for various
clinical outcome prediction tasks. However, applying large language models,
such as BERT-based models, to clinical texts poses two major challenges: the
limitation of input length and the diversity of data sources. This paper
proposes a novel method to preserve the knowledge of long clinical texts using
aggregated ensembles of large language models. Unlike previous studies which
use model ensembling or text aggregation methods separately, we combine
ensemble learning with text aggregation and train multiple large language
models on two clinical outcome tasks: mortality prediction and length of stay
prediction. We show that our method can achieve better results than baselines,
ensembling, and aggregation individually, and can improve the performance of
large language models while handling long inputs and diverse datasets. We
conduct extensive experiments on the admission notes from the MIMIC-III
clinical database by combining multiple unstructured and high-dimensional
datasets, demonstrating our method's effectiveness and superiority over
existing approaches. We also provide a comprehensive analysis and discussion of
our results, highlighting our method's applications and limitations for future
research in the domain of clinical healthcare. The results and analysis of this
study is supportive of our method assisting in clinical healthcare systems by
enabling clinical decision-making with robust performance overcoming the
challenges of long text inputs and varied datasets. | Computational Linguistics |
What field is the article from? | Title: AI for All: Operationalising Diversity and Inclusion Requirements for AI Systems
Abstract: As Artificial Intelligence (AI) permeates many aspects of society, it brings
numerous advantages while at the same time raising ethical concerns and
potential risks, such as perpetuating inequalities through biased or
discriminatory decision-making. To develop AI systems that cater for the needs
of diverse users and uphold ethical values, it is essential to consider and
integrate diversity and inclusion (D&I) principles throughout AI development
and deployment. Requirements engineering (RE) is a fundamental process in
developing software systems by eliciting and specifying relevant needs from
diverse stakeholders. This research aims to address the lack of research and
practice on how to elicit and capture D&I requirements for AI systems. We have
conducted comprehensive data collection and synthesis from the literature
review to extract requirements themes related to D&I in AI. We have proposed a
tailored user story template to capture D&I requirements and conducted focus
group exercises to use the themes and user story template in writing D&I
requirements for two example AI systems. Additionally, we have investigated the
capability of our solution by generating synthetic D&I requirements captured in
user stories with the help of a Large Language Model. | Computers and Society |
What field is the article from? | Title: A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery
Abstract: The trajectory on the road traffic is commonly collected at a low sampling
rate, and trajectory recovery aims to recover a complete and continuous
trajectory from the sparse and discrete inputs. Recently, sequential language
models have been innovatively adopted for trajectory recovery in a pre-trained
manner: it learns road segment representation vectors, which will be used in
the downstream tasks. However, existing methods are incapable of handling
complex trajectories: when the trajectory crosses remote road segments or makes
several turns, which we call critical nodes, the quality of learned
representations deteriorates, and the recovered trajectories skip the critical
nodes. This work is dedicated to offering a more robust trajectory recovery for
complex trajectories. Firstly, we define the trajectory complexity based on the
detour score and entropy score and construct the complexity-aware semantic
graphs correspondingly. Then, we propose a Multi-view Graph and Complexity
Aware Transformer (MGCAT) model to encode these semantics in trajectory
pre-training from two aspects: 1) adaptively aggregate the multi-view graph
features considering trajectory pattern, and 2) higher attention to critical
nodes in a complex trajectory. Such that, our MGCAT is perceptual when handling
the critical scenario of complex trajectories. Extensive experiments are
conducted on large-scale datasets. The results prove that our method learns
better representations for trajectory recovery, with 5.22% higher F1-score
overall and 8.16% higher F1-score for complex trajectories particularly. The
code is available at https://github.com/bonaldli/ComplexTraj. | Machine Learning |
What field is the article from? | Title: Impact of Tokenization on LLaMa Russian Adaptation
Abstract: Latest instruction-tuned large language models (LLM) show great results on
various tasks, however, they often face performance degradation for non-English
input. There is evidence that the reason lies in inefficient tokenization
caused by low language representation in pre-training data which hinders the
comprehension of non-English instructions, limiting the potential of target
language instruction-tuning. In this work we investigate the possibility of
addressing the issue with vocabulary substitution in the context of LLaMa
Russian language adaptation. We explore three variants of vocabulary adaptation
and test their performance on Saiga instruction-tuning and fine-tuning on
Russian Super Glue benchmark. The results of automatic evaluation show that
vocabulary substitution not only improves the model's quality in Russian but
also accelerates fine-tuning (35%) and inference (up to 60%) while reducing
memory consumption. Additional human evaluation of the instruction-tuned models
demonstrates that models with Russian-adapted vocabulary generate answers with
higher user preference than the original Saiga-LLaMa model. | Computational Linguistics |
What field is the article from? | Title: COSMIC: Data Efficient Instruction-tuning For Speech In-Context Learning
Abstract: We present a data and cost efficient way of incorporating the speech modality
into a large language model (LLM). The resulting multi-modal LLM is a
COntextual Speech Model with Instruction-following/in-context-learning
Capabilities - COSMIC. Speech comprehension test question-answer (SQA) pairs
are generated using GPT-3.5 based on the speech transcriptions as a part of the
supervision for the instruction tuning. With fewer than 20M trainable
parameters and as little as 450 hours of English speech data for SQA
generation, COSMIC exhibits emergent instruction-following and in-context
learning capabilities in speech-to-text tasks. The model is able to follow the
given text instructions to generate text response even on the unseen EN$\to$X
speech-to-text translation (S2TT) task with zero-shot setting. We evaluate the
model's in-context learning via various tasks such as EN$\to$X S2TT and
few-shot domain adaptation. And instruction-following capabilities are
evaluated through a contextual biasing benchmark. Our results demonstrate the
efficacy of the proposed low cost recipe for building a speech LLM and that
with the new instruction-tuning data. | Computational Linguistics |
What field is the article from? | Title: PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding
Abstract: Recent advances in text-to-image generation have made remarkable progress in
synthesizing realistic human photos conditioned on given text prompts. However,
existing personalized generation methods cannot simultaneously satisfy the
requirements of high efficiency, promising identity (ID) fidelity, and flexible
text controllability. In this work, we introduce PhotoMaker, an efficient
personalized text-to-image generation method, which mainly encodes an arbitrary
number of input ID images into a stack ID embedding for preserving ID
information. Such an embedding, serving as a unified ID representation, can not
only encapsulate the characteristics of the same input ID comprehensively, but
also accommodate the characteristics of different IDs for subsequent
integration. This paves the way for more intriguing and practically valuable
applications. Besides, to drive the training of our PhotoMaker, we propose an
ID-oriented data construction pipeline to assemble the training data. Under the
nourishment of the dataset constructed through the proposed pipeline, our
PhotoMaker demonstrates better ID preservation ability than test-time
fine-tuning based methods, yet provides significant speed improvements,
high-quality generation results, strong generalization capabilities, and a wide
range of applications. Our project page is available at
https://photo-maker.github.io/ | Computer Vision |
What field is the article from? | Title: ToolTalk: Evaluating Tool-Usage in a Conversational Setting
Abstract: Large language models (LLMs) have displayed massive improvements in reasoning
and decision-making skills and can hold natural conversations with users. Many
recent works seek to augment LLM-based assistants with external tools so they
can access private or up-to-date information and carry out actions on behalf of
users. To better measure the performance of these assistants, this paper
introduces ToolTalk, a benchmark consisting of complex user intents requiring
multi-step tool usage specified through dialogue. ToolTalk contains 28 tools
grouped into 7 plugins, and includes a complete simulated implementation of
each tool, allowing for fully automated evaluation of assistants that rely on
execution feedback. ToolTalk also emphasizes tools that externally affect the
world rather than only tools for referencing or searching information. We
evaluate GPT-3.5 and GPT-4 on ToolTalk resulting in success rates of 26% and
50% respectively. Our analysis of the errors reveals three major categories and
suggests some future directions for improvement. We release ToolTalk at
https://github.com/microsoft/ToolTalk. | Computational Linguistics |
What field is the article from? | Title: CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment
Abstract: Language models trained on large-scale corpus often generate content that is
harmful, toxic, or contrary to human preferences, making their alignment with
human values a critical concern. Reinforcement learning from human feedback
(RLHF) with algorithms like PPO is a prevalent approach for alignment but is
often complex, unstable, and resource-intensive. Recently, ranking-based
alignment methods have emerged, offering stability and effectiveness by
replacing the RL framework with supervised fine-tuning, but they are costly due
to the need for annotated data. Considering that existing large language models
(LLMs) like ChatGPT are already relatively well-aligned and cost-friendly,
researchers have begun to align the language model with human preference from
AI feedback. The common practices, which unidirectionally distill the
instruction-following responses from LLMs, are constrained by their bottleneck.
Thus we introduce CycleAlign to distill alignment capabilities from
parameter-invisible LLMs (black-box) to a parameter-visible model (white-box)
in an iterative manner. With in-context learning (ICL) as the core of the
cycle, the black-box models are able to rank the model-generated responses
guided by human-craft instruction and demonstrations about their preferences.
During iterative interaction, the white-box models also have a judgment about
responses generated by them. Consequently, the agreement ranking could be
viewed as a pseudo label to dynamically update the in-context demonstrations
and improve the preference ranking ability of black-box models. Through
multiple interactions, the CycleAlign framework could align the white-box model
with the black-box model effectively in a low-resource way. Empirical results
illustrate that the model fine-tuned by CycleAlign remarkably exceeds existing
methods, and achieves the state-of-the-art performance in alignment with human
value. | Computational Linguistics |
What field is the article from? | Title: Prediction of Locally Stationary Data Using Expert Advice
Abstract: The problem of continuous machine learning is studied. Within the framework
of the game-theoretic approach, when for calculating the next forecast, no
assumptions about the stochastic nature of the source that generates the data
flow are used -- the source can be analog, algorithmic or probabilistic, its
parameters can change at random times, when building a prognostic model, only
structural assumptions are used about the nature of data generation. An online
forecasting algorithm for a locally stationary time series is presented. An
estimate of the efficiency of the proposed algorithm is obtained. | Machine Learning |
What field is the article from? | Title: Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion
Abstract: Large Language Models (LLMs) excel at tackling various natural language
tasks. However, due to the significant costs involved in re-training or
fine-tuning them, they remain largely static and difficult to personalize.
Nevertheless, a variety of applications could benefit from generations that are
tailored to users' preferences, goals, and knowledge. Among them is web search,
where knowing what a user is trying to accomplish, what they care about, and
what they know can lead to improved search experiences. In this work, we
propose a novel and general approach that augments an LLM with relevant context
from users' interaction histories with a search engine in order to personalize
its outputs. Specifically, we construct an entity-centric knowledge store for
each user based on their search and browsing activities on the web, which is
then leveraged to provide contextually relevant LLM prompt augmentations. This
knowledge store is light-weight, since it only produces user-specific aggregate
projections of interests and knowledge onto public knowledge graphs, and
leverages existing search log infrastructure, thereby mitigating the privacy,
compliance, and scalability concerns associated with building deep user
profiles for personalization. We then validate our approach on the task of
contextual query suggestion, which requires understanding not only the user's
current search context but also what they historically know and care about.
Through a number of experiments based on human evaluation, we show that our
approach is significantly better than several other LLM-powered baselines,
generating query suggestions that are contextually more relevant, personalized,
and useful. | Information Retrieval |
What field is the article from? | Title: A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations
Abstract: Emotion recognition in conversations (ERC), the task of recognizing the
emotion of each utterance in a conversation, is crucial for building empathetic
machines. Existing studies focus mainly on capturing context- and
speaker-sensitive dependencies on the textual modality but ignore the
significance of multimodal information. Different from emotion recognition in
textual conversations, capturing intra- and inter-modal interactions between
utterances, learning weights between different modalities, and enhancing modal
representations play important roles in multimodal ERC. In this paper, we
propose a transformer-based model with self-distillation (SDT) for the task.
The transformer-based model captures intra- and inter-modal interactions by
utilizing intra- and inter-modal transformers, and learns weights between
modalities dynamically by designing a hierarchical gated fusion strategy.
Furthermore, to learn more expressive modal representations, we treat soft
labels of the proposed model as extra training supervision. Specifically, we
introduce self-distillation to transfer knowledge of hard and soft labels from
the proposed model to each modality. Experiments on IEMOCAP and MELD datasets
demonstrate that SDT outperforms previous state-of-the-art baselines. | Artificial Intelligence |
What field is the article from? | Title: Distributional Latent Variable Models with an Application in Active Cognitive Testing
Abstract: Cognitive modeling commonly relies on asking participants to complete a
battery of varied tests in order to estimate attention, working memory, and
other latent variables. In many cases, these tests result in highly variable
observation models. A near-ubiquitous approach is to repeat many observations
for each test, resulting in a distribution over the outcomes from each test
given to each subject. In this paper, we explore the usage of latent variable
modeling to enable learning across many correlated variables simultaneously. We
extend latent variable models (LVMs) to the setting where observed data for
each subject are a series of observations from many different distributions,
rather than simple vectors to be reconstructed. By embedding test battery
results for individuals in a latent space that is trained jointly across a
population, we are able to leverage correlations both between tests for a
single participant and between multiple participants. We then propose an active
learning framework that leverages this model to conduct more efficient
cognitive test batteries. We validate our approach by demonstrating with
real-time data acquisition that it performs comparably to conventional methods
in making item-level predictions with fewer test items. | Artificial Intelligence |
What field is the article from? | Title: GPT-4 Surpassing Human Performance in Linguistic Pragmatics
Abstract: As Large Language Models (LLMs) become increasingly integrated into everyday
life, their capabilities to understand and emulate human cognition are under
steady examination. This study investigates the ability of LLMs to comprehend
and interpret linguistic pragmatics, an aspect of communication that considers
context and implied meanings. Using Grice's communication principles, LLMs and
human subjects (N=76) were evaluated based on their responses to various
dialogue-based tasks. The findings revealed the superior performance and speed
of LLMs, particularly GPT4, over human subjects in interpreting pragmatics.
GPT4 also demonstrated accuracy in the pre-testing of human-written samples,
indicating its potential in text analysis. In a comparative analysis of LLMs
using human individual and average scores, the models exhibited significant
chronological improvement. The models were ranked from lowest to highest score,
with GPT2 positioned at 78th place, GPT3 ranking at 23rd, Bard at 10th, GPT3.5
placing 5th, Best Human scoring 2nd, and GPT4 achieving the top spot. The
findings highlight the remarkable progress made in the development and
performance of these LLMs. Future studies should consider diverse subjects,
multiple languages, and other cognitive aspects to fully comprehend the
capabilities of LLMs. This research holds significant implications for the
development and application of AI-based models in communication-centered
sectors. | Computational Linguistics |
What field is the article from? | Title: Prompt Optimisation with Random Sampling
Abstract: Using the generative nature of a language model to generate task-relevant
separators has shown competitive results compared to human-curated prompts like
"TL;DR". We demonstrate that even randomly chosen tokens from the vocabulary as
separators can achieve near-state-of-the-art performance. We analyse this
phenomenon in detail using three different random generation strategies,
establishing that the language space is rich with potential good separators,
regardless of the underlying language model size. These observations challenge
the common assumption that an effective prompt should be human-readable or
task-relevant. Experimental results show that using random separators leads to
an average 16% relative improvement across nine text classification tasks on
seven language models, compared to human-curated separators, and is on par with
automatic prompt searching methods. | Computational Linguistics |
What field is the article from? | Title: Exploring Parity Challenges in Reinforcement Learning through Curriculum Learning with Noisy Labels
Abstract: This paper delves into applying reinforcement learning (RL) in strategy
games, particularly those characterized by parity challenges, as seen in
specific positions of Go and Chess and a broader range of impartial games. We
propose a simulated learning process, structured within a curriculum learning
framework and augmented with noisy labels, to mirror the intricacies of
self-play learning scenarios. This approach thoroughly analyses how neural
networks (NNs) adapt and evolve from elementary to increasingly complex game
positions. Our empirical research indicates that even minimal label noise can
significantly impede NNs' ability to discern effective strategies, a difficulty
that intensifies with the growing complexity of the game positions. These
findings underscore the urgent need for advanced methodologies in RL training,
specifically tailored to counter the obstacles imposed by noisy evaluations.
The development of such methodologies is crucial not only for enhancing NN
proficiency in strategy games with significant parity elements but also for
broadening the resilience and efficiency of RL systems across diverse and
complex environments. | Artificial Intelligence |
What field is the article from? | Title: minimax: Efficient Baselines for Autocurricula in JAX
Abstract: Unsupervised environment design (UED) is a form of automatic curriculum
learning for training robust decision-making agents to zero-shot transfer into
unseen environments. Such autocurricula have received much interest from the RL
community. However, UED experiments, based on CPU rollouts and GPU model
updates, have often required several weeks of training. This compute
requirement is a major obstacle to rapid innovation for the field. This work
introduces the minimax library for UED training on accelerated hardware. Using
JAX to implement fully-tensorized environments and autocurriculum algorithms,
minimax allows the entire training loop to be compiled for hardware
acceleration. To provide a petri dish for rapid experimentation, minimax
includes a tensorized grid-world based on MiniGrid, in addition to reusable
abstractions for conducting autocurricula in procedurally-generated
environments. With these components, minimax provides strong UED baselines,
including new parallelized variants, which achieve over 120$\times$ speedups in
wall time compared to previous implementations when training with equal batch
sizes. The minimax library is available under the Apache 2.0 license at
https://github.com/facebookresearch/minimax. | Machine Learning |
What field is the article from? | Title: Responsible AI Research Needs Impact Statements Too
Abstract: All types of research, development, and policy work can have unintended,
adverse consequences - work in responsible artificial intelligence (RAI),
ethical AI, or ethics in AI is no exception. | Artificial Intelligence |
What field is the article from? | Title: Infinite forecast combinations based on Dirichlet process
Abstract: Forecast combination integrates information from various sources by
consolidating multiple forecast results from the target time series. Instead of
the need to select a single optimal forecasting model, this paper introduces a
deep learning ensemble forecasting model based on the Dirichlet process.
Initially, the learning rate is sampled with three basis distributions as
hyperparameters to convert the infinite mixture into a finite one. All
checkpoints are collected to establish a deep learning sub-model pool, and
weight adjustment and diversity strategies are developed during the combination
process. The main advantage of this method is its ability to generate the
required base learners through a single training process, utilizing the
decaying strategy to tackle the challenge posed by the stochastic nature of
gradient descent in determining the optimal learning rate. To ensure the
method's generalizability and competitiveness, this paper conducts an empirical
analysis using the weekly dataset from the M4 competition and explores
sensitivity to the number of models to be combined. The results demonstrate
that the ensemble model proposed offers substantial improvements in prediction
accuracy and stability compared to a single benchmark model. | Machine Learning |
What field is the article from? | Title: I Was Blind but Now I See: Implementing Vision-Enabled Dialogue in Social Robots
Abstract: In the rapidly evolving landscape of human-computer interaction, the
integration of vision capabilities into conversational agents stands as a
crucial advancement. This paper presents an initial implementation of a
dialogue manager that leverages the latest progress in Large Language Models
(e.g., GPT-4, IDEFICS) to enhance the traditional text-based prompts with
real-time visual input. LLMs are used to interpret both textual prompts and
visual stimuli, creating a more contextually aware conversational agent. The
system's prompt engineering, incorporating dialogue with summarisation of the
images, ensures a balance between context preservation and computational
efficiency. Six interactions with a Furhat robot powered by this system are
reported, illustrating and discussing the results obtained. By implementing
this vision-enabled dialogue system, the paper envisions a future where
conversational agents seamlessly blend textual and visual modalities, enabling
richer, more context-aware dialogues. | Robotics |
What field is the article from? | Title: Using Curiosity for an Even Representation of Tasks in Continual Offline Reinforcement Learning
Abstract: In this work, we investigate the means of using curiosity on replay buffers
to improve offline multi-task continual reinforcement learning when tasks,
which are defined by the non-stationarity in the environment, are non labeled
and not evenly exposed to the learner in time. In particular, we investigate
the use of curiosity both as a tool for task boundary detection and as a
priority metric when it comes to retaining old transition tuples, which we
respectively use to propose two different buffers. Firstly, we propose a Hybrid
Reservoir Buffer with Task Separation (HRBTS), where curiosity is used to
detect task boundaries that are not known due to the task agnostic nature of
the problem. Secondly, by using curiosity as a priority metric when it comes to
retaining old transition tuples, a Hybrid Curious Buffer (HCB) is proposed. We
ultimately show that these buffers, in conjunction with regular reinforcement
learning algorithms, can be used to alleviate the catastrophic forgetting issue
suffered by the state of the art on replay buffers when the agent's exposure to
tasks is not equal along time. We evaluate catastrophic forgetting and the
efficiency of our proposed buffers against the latest works such as the Hybrid
Reservoir Buffer (HRB) and the Multi-Time Scale Replay Buffer (MTR) in three
different continual reinforcement learning settings. Experiments were done on
classical control tasks and Metaworld environment. Experiments show that our
proposed replay buffers display better immunity to catastrophic forgetting
compared to existing works in most of the settings. | Machine Learning |
What field is the article from? | Title: FFINet: Future Feedback Interaction Network for Motion Forecasting
Abstract: Motion forecasting plays a crucial role in autonomous driving, with the aim
of predicting the future reasonable motions of traffic agents. Most existing
methods mainly model the historical interactions between agents and the
environment, and predict multi-modal trajectories in a feedforward process,
ignoring potential trajectory changes caused by future interactions between
agents. In this paper, we propose a novel Future Feedback Interaction Network
(FFINet) to aggregate features the current observations and potential future
interactions for trajectory prediction. Firstly, we employ different
spatial-temporal encoders to embed the decomposed position vectors and the
current position of each scene, providing rich features for the subsequent
cross-temporal aggregation. Secondly, the relative interaction and
cross-temporal aggregation strategies are sequentially adopted to integrate
features in the current fusion module, observation interaction module, future
feedback module and global fusion module, in which the future feedback module
can enable the understanding of pre-action by feeding the influence of preview
information to feedforward prediction. Thirdly, the comprehensive interaction
features are further fed into final predictor to generate the joint predicted
trajectories of multiple agents. Extensive experimental results show that our
FFINet achieves the state-of-the-art performance on Argoverse 1 and Argoverse 2
motion forecasting benchmarks. | Computer Vision |
What field is the article from? | Title: Do large language models and humans have similar behaviors in causal inference with script knowledge?
Abstract: Recently, large pre-trained language models (LLMs) have demonstrated superior
language understanding abilities, including zero-shot causal reasoning.
However, it is unclear to what extent their capabilities are similar to human
ones. We here study the processing of an event $B$ in a script-based story,
which causally depends on a previous event $A$. In our manipulation, event $A$
is stated, negated, or omitted in an earlier section of the text. We first
conducted a self-paced reading experiment, which showed that humans exhibit
significantly longer reading times when causal conflicts exist ($\neg A
\rightarrow B$) than under logical conditions ($A \rightarrow B$). However,
reading times remain similar when cause A is not explicitly mentioned,
indicating that humans can easily infer event B from their script knowledge. We
then tested a variety of LLMs on the same data to check to what extent the
models replicate human behavior. Our experiments show that 1) only recent LLMs,
like GPT-3 or Vicuna, correlate with human behavior in the $\neg A \rightarrow
B$ condition. 2) Despite this correlation, all models still fail to predict
that $nil \rightarrow B$ is less surprising than $\neg A \rightarrow B$,
indicating that LLMs still have difficulties integrating script knowledge. Our
code and collected data set are available at
https://github.com/tony-hong/causal-script. | Computational Linguistics |
What field is the article from? | Title: Language-Guided Transformer for Federated Multi-Label Classification
Abstract: Federated Learning (FL) is an emerging paradigm that enables multiple users
to collaboratively train a robust model in a privacy-preserving manner without
sharing their private data. Most existing approaches of FL only consider
traditional single-label image classification, ignoring the impact when
transferring the task to multi-label image classification. Nevertheless, it is
still challenging for FL to deal with user heterogeneity in their local data
distribution in the real-world FL scenario, and this issue becomes even more
severe in multi-label image classification. Inspired by the recent success of
Transformers in centralized settings, we propose a novel FL framework for
multi-label classification. Since partial label correlation may be observed by
local clients during training, direct aggregation of locally updated models
would not produce satisfactory performances. Thus, we propose a novel FL
framework of Language-Guided Transformer (FedLGT) to tackle this challenging
task, which aims to exploit and transfer knowledge across different clients for
learning a robust global model. Through extensive experiments on various
multi-label datasets (e.g., FLAIR, MS-COCO, etc.), we show that our FedLGT is
able to achieve satisfactory performance and outperforms standard FL techniques
under multi-label FL scenarios. Code is available at
https://github.com/Jack24658735/FedLGT. | Computer Vision |
What field is the article from? | Title: Optimizing Fault-Tolerant Quality-Guaranteed Sensor Deployments for UAV Localization in Critical Areas via Computational Geometry
Abstract: The increasing spreading of small commercial Unmanned Aerial Vehicles (UAVs,
aka drones) presents serious threats for critical areas such as airports, power
plants, governmental and military facilities. In fact, such UAVs can easily
disturb or jam radio communications, collide with other flying objects, perform
espionage activity, and carry offensive payloads, e.g., weapons or explosives.
A central problem when designing surveillance solutions for the localization of
unauthorized UAVs in critical areas is to decide how many triangulating sensors
to use, and where to deploy them to optimise both coverage and cost
effectiveness.
In this article, we compute deployments of triangulating sensors for UAV
localization, optimizing a given blend of metrics, namely: coverage under
multiple sensing quality levels, cost-effectiveness, fault-tolerance. We focus
on large, complex 3D regions, which exhibit obstacles (e.g., buildings),
varying terrain elevation, different coverage priorities, constraints on
possible sensors placement. Our novel approach relies on computational geometry
and statistical model checking, and enables the effective use of off-the-shelf
AI-based black-box optimizers. Moreover, our method allows us to compute a
closed-form, analytical representation of the region uncovered by a sensor
deployment, which provides the means for rigorous, formal certification of the
quality of the latter.
We show the practical feasibility of our approach by computing optimal sensor
deployments for UAV localization in two large, complex 3D critical regions, the
Rome Leonardo Da Vinci International Airport (FCO) and the Vienna International
Center (VIC), using NOMAD as our state-of-the-art underlying optimization
engine. Results show that we can compute optimal sensor deployments within a
few hours on a standard workstation and within minutes on a small parallel
infrastructure. | Robotics |
What field is the article from? | Title: Learning county from pixels: Corn yield prediction with attention-weighted multiple instance learning
Abstract: Remote sensing technology has become a promising tool in yield prediction.
Most prior work employs satellite imagery for county-level corn yield
prediction by spatially aggregating all pixels within a county into a single
value, potentially overlooking the detailed information and valuable insights
offered by more granular data. To this end, this research examines each county
at the pixel level and applies multiple instance learning to leverage detailed
information within a county. In addition, our method addresses the "mixed
pixel" issue caused by the inconsistent resolution between feature datasets and
crop mask, which may introduce noise into the model and therefore hinder
accurate yield prediction. Specifically, the attention mechanism is employed to
automatically assign weights to different pixels, which can mitigate the
influence of mixed pixels. The experimental results show that the developed
model outperforms four other machine learning models over the past five years
in the U.S. corn belt and demonstrates its best performance in 2022, achieving
a coefficient of determination (R2) value of 0.84 and a root mean square error
(RMSE) of 0.83. This paper demonstrates the advantages of our approach from
both spatial and temporal perspectives. Furthermore, through an in-depth study
of the relationship between mixed pixels and attention, it is verified that our
approach can capture critical feature information while filtering out noise
from mixed pixels. | Computer Vision |
What field is the article from? | Title: Training Robust Deep Physiological Measurement Models with Synthetic Video-based Data
Abstract: Recent advances in supervised deep learning techniques have demonstrated the
possibility to remotely measure human physiological vital signs (e.g.,
photoplethysmograph, heart rate) just from facial videos. However, the
performance of these methods heavily relies on the availability and diversity
of real labeled data. Yet, collecting large-scale real-world data with
high-quality labels is typically challenging and resource intensive, which also
raises privacy concerns when storing personal bio-metric data. Synthetic
video-based datasets (e.g., SCAMPS \cite{mcduff2022scamps}) with
photo-realistic synthesized avatars are introduced to alleviate the issues
while providing high-quality synthetic data. However, there exists a
significant gap between synthetic and real-world data, which hinders the
generalization of neural models trained on these synthetic datasets. In this
paper, we proposed several measures to add real-world noise to synthetic
physiological signals and corresponding facial videos. We experimented with
individual and combined augmentation methods and evaluated our framework on
three public real-world datasets. Our results show that we were able to reduce
the average MAE from 6.9 to 2.0. | Computer Vision |
What field is the article from? | Title: Uncovering communities of pipelines in the task-fMRI analytical space
Abstract: Functional magnetic resonance imaging analytical workflows are highly
flexible with no definite consensus on how to choose a pipeline. While methods
have been developed to explore this analytical space, there is still a lack of
understanding of the relationships between the different pipelines. We use
community detection algorithms to explore the pipeline space and assess its
stability across different contexts. We show that there are subsets of
pipelines that give similar results, especially those sharing specific
parameters (e.g. number of motion regressors, software packages, etc.), with
relative stability across groups of participants. By visualizing the
differences between these subsets, we describe the effect of pipeline
parameters and derive general relationships in the analytical space. | Artificial Intelligence |
What field is the article from? | Title: Generating Interpretable Networks using Hypernetworks
Abstract: An essential goal in mechanistic interpretability to decode a network, i.e.,
to convert a neural network's raw weights to an interpretable algorithm. Given
the difficulty of the decoding problem, progress has been made to understand
the easier encoding problem, i.e., to convert an interpretable algorithm into
network weights. Previous works focus on encoding existing algorithms into
networks, which are interpretable by definition. However, focusing on encoding
limits the possibility of discovering new algorithms that humans have never
stumbled upon, but that are nevertheless interpretable. In this work, we
explore the possibility of using hypernetworks to generate interpretable
networks whose underlying algorithms are not yet known. The hypernetwork is
carefully designed such that it can control network complexity, leading to a
diverse family of interpretable algorithms ranked by their complexity. All of
them are interpretable in hindsight, although some of them are less intuitive
to humans, hence providing new insights regarding how to "think" like a neural
network. For the task of computing L1 norms, hypernetworks find three
algorithms: (a) the double-sided algorithm, (b) the convexity algorithm, (c)
the pudding algorithm, although only the first algorithm was expected by the
authors before experiments. We automatically classify these algorithms and
analyze how these algorithmic phases develop during training, as well as how
they are affected by complexity control. Furthermore, we show that a trained
hypernetwork can correctly construct models for input dimensions not seen in
training, demonstrating systematic generalization. | Machine Learning |
What field is the article from? | Title: Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation
Abstract: Deep reinforcement learning (DRL) provides a promising way for intelligent
agents (e.g., autonomous vehicles) to learn to navigate complex scenarios.
However, DRL with neural networks as function approximators is typically
considered a black box with little explainability and often suffers from
suboptimal performance, especially for autonomous navigation in highly
interactive multi-agent environments. To address these issues, we propose three
auxiliary tasks with spatio-temporal relational reasoning and integrate them
into the standard DRL framework, which improves the decision making performance
and provides explainable intermediate indicators. We propose to explicitly
infer the internal states (i.e., traits and intentions) of surrounding agents
(e.g., human drivers) as well as to predict their future trajectories in the
situations with and without the ego agent through counterfactual reasoning.
These auxiliary tasks provide additional supervision signals to infer the
behavior patterns of other interactive agents. Multiple variants of framework
integration strategies are compared. We also employ a spatio-temporal graph
neural network to encode relations between dynamic entities, which enhances
both internal state inference and decision making of the ego agent. Moreover,
we propose an interactivity estimation mechanism based on the difference
between predicted trajectories in these two situations, which indicates the
degree of influence of the ego agent on other agents. To validate the proposed
method, we design an intersection driving simulator based on the Intelligent
Intersection Driver Model (IIDM) that simulates vehicles and pedestrians. Our
approach achieves robust and state-of-the-art performance in terms of standard
evaluation metrics and provides explainable intermediate indicators (i.e.,
internal states, and interactivity scores) for decision making. | Robotics |
What field is the article from? | Title: AI-TA: Towards an Intelligent Question-Answer Teaching Assistant using Open-Source LLMs
Abstract: Responding to the thousands of student questions on online QA platforms each
semester has a considerable human cost, particularly in computing courses with
rapidly growing enrollments. To address the challenges of scalable and
intelligent question-answering (QA), we introduce an innovative solution that
leverages open-source Large Language Models (LLMs) from the LLaMA-2 family to
ensure data privacy. Our approach combines augmentation techniques such as
retrieval augmented generation (RAG), supervised fine-tuning (SFT), and
learning from human preferences data using Direct Preference Optimization
(DPO). Through extensive experimentation on a Piazza dataset from an
introductory CS course, comprising 10,000 QA pairs and 1,500 pairs of
preference data, we demonstrate a significant 30% improvement in the quality of
answers, with RAG being a particularly impactful addition. Our contributions
include the development of a novel architecture for educational QA, extensive
evaluations of LLM performance utilizing both human assessments and LLM-based
metrics, and insights into the challenges and future directions of educational
data processing. This work paves the way for the development of AI-TA, an
intelligent QA assistant customizable for courses with an online QA platform | Machine Learning |
What field is the article from? | Title: Bergeron: Combating Adversarial Attacks through a Conscience-Based Alignment Framework
Abstract: Modern Large language models (LLMs) can still generate responses that may not
be aligned with human expectations or values. While many weight-based alignment
methods have been proposed, many of them still leave models vulnerable to
attacks when used on their own. To help mitigate this issue, we introduce
Bergeron, a framework designed to improve the robustness of LLMs against
adversarial attacks. Bergeron employs a two-tiered architecture. Here, a
secondary LLM serves as a simulated conscience that safeguards a primary LLM.
We do this by monitoring for and correcting potentially harmful text within
both the prompt inputs and the generated outputs of the primary LLM. Empirical
evaluation shows that Bergeron can improve the alignment and robustness of
several popular LLMs without costly fine-tuning. It aids both open-source and
black-box LLMs by complementing and reinforcing their existing alignment
training. | Cryptography and Security |
What field is the article from? | Title: Towards Generalized Multi-stage Clustering: Multi-view Self-distillation
Abstract: Existing multi-stage clustering methods independently learn the salient
features from multiple views and then perform the clustering task.
Particularly, multi-view clustering (MVC) has attracted a lot of attention in
multi-view or multi-modal scenarios. MVC aims at exploring common semantics and
pseudo-labels from multiple views and clustering in a self-supervised manner.
However, limited by noisy data and inadequate feature learning, such a
clustering paradigm generates overconfident pseudo-labels that mis-guide the
model to produce inaccurate predictions. Therefore, it is desirable to have a
method that can correct this pseudo-label mistraction in multi-stage clustering
to avoid the bias accumulation. To alleviate the effect of overconfident
pseudo-labels and improve the generalization ability of the model, this paper
proposes a novel multi-stage deep MVC framework where multi-view
self-distillation (DistilMVC) is introduced to distill dark knowledge of label
distribution. Specifically, in the feature subspace at different hierarchies,
we explore the common semantics of multiple views through contrastive learning
and obtain pseudo-labels by maximizing the mutual information between views.
Additionally, a teacher network is responsible for distilling pseudo-labels
into dark knowledge, supervising the student network and improving its
predictive capabilities to enhance the robustness. Extensive experiments on
real-world multi-view datasets show that our method has better clustering
performance than state-of-the-art methods. | Computer Vision |
What field is the article from? | Title: Transformer as Linear Expansion of Learngene
Abstract: We propose expanding the shared Transformer module to produce and initialize
Transformers with diverse depths, enabling adaptation to dynamic resource
constraints. Drawing an analogy to genetic expansibility, we term such module
as learngene. To identify the expansion mechanism, we delve into the
relationship between the layer position and its corresponding weight value, and
find that linear function appropriately approximates this relationship.
Building on this insight, we present Transformer as Linear Expansion of
learnGene (TLEG), a novel approach for flexibly producing and initializing
Transformers of diverse depths. Specifically, to learn learngene, we firstly
construct an auxiliary Transformer linearly expanded from learngene, after
which we train it through employing soft distillation. Subsequently, we can
produce and initialize Transformers of varying depths via linearly expanding
the well-trained learngene, thereby supporting diverse downstream scenarios.
Extensive experiments on ImageNet-1K classification demonstrate that TLEG
achieves comparable or better performance compared to many individual models
trained from scratch, while reducing around 2$\times$ training cost. When
transferring one model to several downstream classification datasets, TLEG
surpasses existing initialization methods by a large margin (e.g., +6.87% on
iNat 2019 and +7.66% on CIFAR-100). Under the situation where we need to
produce models of different scales adapting for different resource constraints,
TLEG achieves comparable results while reducing around 19$\times$ parameters
stored to initialize these models and around 5$\times$ training costs, in
contrast to the pre-training and fine-tuning approach. | Artificial Intelligence |
What field is the article from? | Title: Class-Incremental Continual Learning for General Purpose Healthcare Models
Abstract: Healthcare clinics regularly encounter dynamic data that changes due to
variations in patient populations, treatment policies, medical devices, and
emerging disease patterns. Deep learning models can suffer from catastrophic
forgetting when fine-tuned in such scenarios, causing poor performance on
previously learned tasks. Continual learning allows learning on new tasks
without performance drop on previous tasks. In this work, we investigate the
performance of continual learning models on four different medical imaging
scenarios involving ten classification datasets from diverse modalities,
clinical specialties, and hospitals. We implement various continual learning
approaches and evaluate their performance in these scenarios. Our results
demonstrate that a single model can sequentially learn new tasks from different
specialties and achieve comparable performance to naive methods. These findings
indicate the feasibility of recycling or sharing models across the same or
different medical specialties, offering another step towards the development of
general-purpose medical imaging AI that can be shared across institutions. | Machine Learning |
What field is the article from? | Title: Divide-and-Conquer Strategy for Large-Scale Dynamic Bayesian Network Structure Learning
Abstract: Dynamic Bayesian Networks (DBNs), renowned for their interpretability, have
become increasingly vital in representing complex stochastic processes in
various domains such as gene expression analysis, healthcare, and traffic
prediction. Structure learning of DBNs from data is challenging, particularly
for datasets with thousands of variables. Most current algorithms for DBN
structure learning are adaptations from those used in static Bayesian Networks
(BNs), and are typically focused on small-scale problems. In order to solve
large-scale problems while taking full advantage of existing algorithms, this
paper introduces a novel divide-and-conquer strategy, originally developed for
static BNs, and adapts it for large-scale DBN structure learning. In this work,
we specifically concentrate on 2 Time-sliced Bayesian Networks (2-TBNs), a
special class of DBNs. Furthermore, we leverage the prior knowledge of 2-TBNs
to enhance the performance of the strategy we introduce. Our approach
significantly improves the scalability and accuracy of 2-TBN structure
learning. Experimental results demonstrate the effectiveness of our method,
showing substantial improvements over existing algorithms in both computational
efficiency and structure learning accuracy. On problem instances with more than
1,000 variables, our approach improves two accuracy metrics by 74.45% and
110.94% on average , respectively, while reducing runtime by 93.65% on average. | Machine Learning |
What field is the article from? | Title: A Framework for Realistic Simulation of Daily Human Activity
Abstract: For social robots like Astro which interact with and adapt to the daily
movements of users within the home, realistic simulation of human activity is
needed for feature development and testing. This paper presents a framework for
simulating daily human activity patterns in home environments at scale,
supporting manual configurability of different personas or activity patterns,
variation of activity timings, and testing on multiple home layouts. We
introduce a method for specifying day-to-day variation in schedules and present
a bidirectional constraint propagation algorithm for generating schedules from
templates. We validate the expressive power of our framework through a use case
scenario analysis and demonstrate that our method can be used to generate data
closely resembling human behavior from three public datasets and a
self-collected dataset. Our contribution supports systematic testing of social
robot behaviors at scale, enables procedural generation of synthetic datasets
of human movement in different households, and can help minimize bias in
training data, leading to more robust and effective robots for home
environments. | Robotics |
What field is the article from? | Title: Stable Diffusion Reference Only: Image Prompt and Blueprint Jointly Guided Multi-Condition Diffusion Model for Secondary Painting
Abstract: Stable Diffusion and ControlNet have achieved excellent results in the field
of image generation and synthesis. However, due to the granularity and method
of its control, the efficiency improvement is limited for professional artistic
creations such as comics and animation production whose main work is secondary
painting. In the current workflow, fixing characters and image styles often
need lengthy text prompts, and even requires further training through
TextualInversion, DreamBooth or other methods, which is very complicated and
expensive for painters. Therefore, we present a new method in this paper,
Stable Diffusion Reference Only, a images-to-image self-supervised model that
uses only two types of conditional images for precise control generation to
accelerate secondary painting. The first type of conditional image serves as an
image prompt, supplying the necessary conceptual and color information for
generation. The second type is blueprint image, which controls the visual
structure of the generated image. It is natively embedded into the original
UNet, eliminating the need for ControlNet. We released all the code for the
module and pipeline, and trained a controllable character line art coloring
model at https://github.com/aihao2000/stable-diffusion-reference-only, that
achieved state-of-the-art results in this field. This verifies the
effectiveness of the structure and greatly improves the production efficiency
of animations, comics, and fanworks. | Computer Vision |