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d249431362 | State-of-the-art text classification models are becoming increasingly reliant on deep neural networks (DNNs). Due to their black-box nature, faithful and robust explanation methods need to accompany classifiers for deployment in reallife scenarios. However, it has been shown in vision applications that explanation meth... | FOOLING EXPLANATIONS IN TEXT CLASSIFIERS |
d263831268 | In this paper, we investigate the behavior of gradient descent algorithms in physics-informed machine learning methods like PINNs, which minimize residuals connected to partial differential equations (PDEs).Our key result is that the difficulty in training these models is closely related to the conditioning of a specif... | AN OPERATOR PRECONDITIONING PERSPECTIVE ON TRAINING IN PHYSICS-INFORMED MACHINE LEARNING |
d53100211 | Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neural network learning to more human realistic settings. In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off ... | LEARNING TO LEARN WITHOUT FORGETTING BY MAXIMIZING TRANSFER AND MINIMIZING INTERFERENCE |
d247411282 | Recently, post-training quantization (PTQ) has driven much attention to produce efficient neural networks without long-time retraining. Despite its low cost, current PTQ works tend to fail under the extremely low-bit setting. In this study, we pioneeringly confirm that properly incorporating activation quantization int... | QDROP: RANDOMLY DROPPING QUANTIZATION FOR EXTREMELY LOW-BIT POST-TRAINING QUANTIZATION |
d50783524 | Human annotation for syntactic parsing is expensive, and large resources are available only for a fraction of languages. A question we ask is whether one can leverage abundant unlabeled texts to improve syntactic parsers, beyond just using the texts to obtain more generalisable lexical features (i.e. beyond word embedd... | Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder |
d3538627 | Questions that require counting a variety of objects in images remain a major challenge in visual question answering (VQA). The most common approaches to VQA involve either classifying answers based on fixed length representations of both the image and question or summing fractional counts estimated from each section o... | INTERPRETABLE COUNTING FOR VISUAL QUESTION ANSWERING |
d259298593 | The generalization ability of machine learning models degrades significantly when the test distribution shifts away from the training distribution.We investigate the problem of training models that are robust to shifts caused by changes in the distribution of class-priors or group-priors.The presence of skewed training... | DISTRIBUTIONALLY ROBUST POST-HOC CLASSIFIERS UNDER PRIOR SHIFTS |
d249395519 | How the brain performs credit assignment is a fundamental unsolved problem in neuroscience. Many 'biologically plausible' algorithms have been proposed, which compute gradients that approximate those computed by backpropagation (BP), and which operate in ways that more closely satisfy the constraints imposed by neural ... | BACKPROPAGATION AT THE INFINITESIMAL INFERENCE LIMIT OF ENERGY-BASED MODELS: UNIFYING PREDICTIVE CODING, EQUILIBRIUM PROPAGATION, AND CONTRASTIVE HEBBIAN LEARNING |
d3474156 | Reading comprehension is a challenging task, especially when executed across longer or across multiple evidence documents, where the answer is likely to reoccur. Existing neural architectures typically do not scale to the entire evidence, and hence, resort to selecting a single passage in the document (either via trunc... | MULTI-MENTION LEARNING FOR READING COMPREHENSION WITH NEURAL CASCADES |
d220525844 | A hallmark of human intelligence is the ability to construct self-contained chunks of knowledge and adequately reuse them in novel combinations for solving different yet structurally related problems. Learning such compositional structures has been a significant challenge for artificial systems, due to the combinatoria... | Lifelong Learning of Compositional Structures |
d260505344 | Vision transformers (ViTs) process input images as sequences of patches via selfattention; a radically different architecture than convolutional neural networks (CNNs). This makes it interesting to study the adversarial feature space of ViT models and their transferability. In particular, we observe that adversarial pa... | ON IMPROVING ADVERSARIAL TRANSFERABILITY OF VISION TRANSFORMERS |
d226964491 | Pre-trained language models (PTLM) have achieved impressive results in a range of natural language understanding (NLU) and generation (NLG) tasks. However, current pre-training objectives such as masked token prediction (for BERT-style PTLMs) and masked span infilling (for T5-style PTLMs) do not explicitly model the re... | PRE-TRAINING TEXT-TO-TEXT TRANSFORMERS FOR CONCEPT-CENTRIC COMMON SENSE |
d208513914 | For sequence models with large vocabularies, a majority of network parameters lie in the input and output layers. In this work, we describe a new method, DeFINE, for learning deep token representations efficiently. Our architecture uses a hierarchical structure with novel skip-connections which allows for the use of lo... | DEFINE: DEEP FACTORIZED INPUT TOKEN EMBEDDINGS FOR NEURAL SEQUENCE MODELING |
d67877096 | Numerous past works have tackled the problem of task-driven navigation. But, how to effectively explore a new environment to enable a variety of down-stream tasks has received much less attention. In this work, we study how agents can autonomously explore realistic and complex 3D environments without the context of tas... | LEARNING EXPLORATION POLICIES FOR NAVIGATION |
d57373913 | In many real-world learning scenarios, features are only acquirable at a cost constrained under a budget. In this paper, we propose a novel approach for costsensitive feature acquisition at the prediction-time. The suggested method acquires features incrementally based on a context-aware feature-value function. We form... | OPPORTUNISTIC LEARNING: BUDGETED COST-SENSITIVE LEARNING FROM DATA STREAMS |
d211003742 | Conventional Generative Adversarial Networks (GANs) for text generation tend to have issues of reward sparsity and mode collapse that affect the quality and diversity of generated samples. To address the issues, we propose a novel self-adversarial learning (SAL) paradigm for improving GANs' performance in text generati... | SELF-ADVERSARIAL LEARNING WITH COMPARATIVE DISCRIMINATION FOR TEXT GENERATION |
d253098907 | Deformable Object Manipulation (DOM) is of significant importance to both daily and industrial applications. Recent successes in differentiable physics simulators allow learning algorithms to train a policy with analytic gradients through environment dynamics, which significantly facilitates the development of DOM algo... | DAXBENCH: BENCHMARKING DEFORMABLE OBJECT MANIPULATION WITH DIFFERENTIABLE PHYSICS |
d231749906 | As large-scale graphs become increasingly more prevalent, it poses significant computational challenges to process, extract and analyze large graph data. Graph coarsening is one popular technique to reduce the size of a graph while maintaining essential properties. Despite rich graph coarsening literature, there is onl... | GRAPH COARSENING WITH NEURAL NETWORKS |
d231846526 | Model Agnostic Meta-Learning (MAML) has emerged as a standard framework for meta-learning, where a meta-model is learned with the ability of fast adapting to new tasks. However, as a double-looped optimization problem, MAML needs to differentiate through the whole inner-loop optimization path for every outer-loop train... | META-LEARNING WITH NEURAL TANGENT KERNELS |
d53115163 | We introduce an exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed. The bonus is the error of a neural network predicting features of the observations given by a fixed randomly initialized neural network. We also introduce a method ... | EXPLORATION BY RANDOM NETWORK DISTILLATION |
d3278107 | Deep neural networks have advanced the state of the art in named entity recognition. However, under typical training procedures, advantages over classical methods emerge only with large datasets. As a result, deep learning is employed only when large public datasets or a large budget for manually labeling data is avail... | Deep Active Learning for Named Entity Recognition |
d251929117 | High-definition (HD) map provides abundant and precise environmental information of the driving scene, serving as a fundamental and indispensable component for planning in autonomous driving system. We present MapTR, a structured end-to-end Transformer for efficient online vectorized HD map construction. We propose a u... | MAPTR: STRUCTURED MODELING AND LEARNING FOR ONLINE VECTORIZED HD MAP CONSTRUCTION |
d263608710 | We investigate the unsupervised constituency parsing task, which organizes words and phrases of a sentence into a hierarchical structure without using linguistically annotated data.We observe that existing unsupervised parsers capture differing aspects of parsing structures, which can be leveraged to enhance unsupervis... | Ensemble Distillation for Unsupervised Constituency Parsing |
d248266593 | We study the theory of neural network (NN) from the lens of classical nonparametric regression problems with a focus on NN's ability to adaptively estimate functions with heterogeneous smoothness -a property of functions in Besov or Bounded Variation (BV) classes. Existing work on this problem requires tuning the NN ar... | Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive? |
d249063147 | Direct speech-to-speech translation (S2ST) with discrete units leverages recent progress in speech representation learning. Specifically, a sequence of discrete representations derived in a self-supervised manner are predicted from the model and passed to a vocoder for speech reconstruction, while still facing the foll... | TRANSPEECH: SPEECH-TO-SPEECH TRANSLATION WITH BILATERAL PERTURBATION |
d237605219 | Trust region methods rigorously enabled reinforcement learning (RL) agents to learn monotonically improving policies, leading to superior performance on a variety of tasks. Unfortunately, when it comes to multi-agent reinforcement learning (MARL), the property of monotonic improvement may not simply apply; this is beca... | TRUST REGION POLICY OPTIMISATION IN MULTI-AGENT REINFORCEMENT LEARNING |
d259262449 | In this paper, we explore the application of mean field theory, a technique from statistical physics, to deep metric learning and address the high training complexity commonly associated with conventional metric learning loss functions. By adapting mean field theory for deep metric learning, we develop an approach to d... | Mean Field Theory in Deep Metric Learning |
d238744509 | We propose a novel prediction interval (PI) method for uncertainty quantification, which addresses three major issues with the state-of-the-art PI methods. First, existing PI methods require retraining of neural networks (NNs) for every given confidence level and suffer from the crossing issue in calculating multiple P... | PI3NN: OUT-OF-DISTRIBUTION-AWARE PREDICTION INTERVALS FROM THREE NEURAL NETWORKS |
d49416020 | Stability is a key aspect of data analysis. In many applications, the natural notion of stability is geometric, as illustrated for example in computer vision. Scattering transforms construct deep convolutional representations which are certified stable to input deformations. This stability to deformations can be interp... | DIFFUSION SCATTERING TRANSFORMS ON GRAPHS |
d235490479 | Self-and mutually-exciting point processes are popular models in machine learning and statistics for dependent discrete event data. To date, most existing models assume stationary kernels (including the classical Hawkes processes) and simple parametric models. Modern applications with complex event data require more ge... | NEURAL SPECTRAL MARKED POINT PROCESSES |
d237491843 | We present a novel adaptive optimization algorithm for large-scale machine learning problems. Equipped with a low-cost estimate of local curvature and Lipschitz smoothness, our method dynamically adapts the search direction and step-size. The search direction contains gradient information preconditioned by a well-scale... | Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information |
d256615445 | In this work, we define a diffusion-based generative model capable of both music synthesis and source separation by learning the score of the joint probability density of sources sharing a context. Alongside the classic total inference tasks (i.e., generating a mixture, separating the sources), we also introduce and ex... | Multi-Source Diffusion Models for Simultaneous Music Generation and Separation |
d226236734 | In this paper, we explore connections between interpretable machine learning and learning theory through the lens of local approximation explanations. First, we tackle the traditional problem of performance generalization and bound the testtime accuracy of a model using a notion of how locally explainable it is. Second... | A LEARNING THEORETIC PERSPECTIVE ON LOCAL EXPLAINABILITY |
d231933756 | Back-translation (Sennrich et al., 2016)is an effective strategy to improve the performance of Neural Machine Translation (NMT) by generating pseudo-parallel data. However, several recent works have found that better translation quality of the pseudo-parallel data does not necessarily lead to better final translation m... | META BACK-TRANSLATION |
d250408279 | In this paper, we leverage low-level compiler intermediate representations (IR) to improve code translation. Traditional transpilers rely on syntactic information and handcrafted rules, which limits their applicability and produces unnaturallooking code. Applying neural machine translation (NMT) approaches to code ha... | CODE TRANSLATION WITH COMPILER REPRESENTATIONS |
d263609114 | This paper rigorously shows how over-parameterization dramatically changes the convergence behaviors of gradient descent (GD) for the matrix sensing problem, where the goal is to recover an unknown low-rank ground-truth matrix from near-isotropic linear measurements. First, we consider the symmetric setting with the sy... | How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization |
d227162420 | Multi-goal reaching is an important problem in reinforcement learning needed to achieve algorithmic generalization. Despite recent advances in this field, current algorithms suffer from three major challenges: high sample complexity, learning only a single way of reaching the goals, and difficulties in solving complex ... | C-LEARNING: HORIZON-AWARE CUMULATIVE ACCESSIBILITY ESTIMATION |
d263830324 | Scaling up representations for images or text has been extensively investigated in the past few years and has led to revolutions in learning vision and language.However, scalable representation for 3D objects and scenes is relatively unexplored.In this work, we present Uni3D, a 3D foundation model to explore the unifie... | UNI3D: EXPLORING UNIFIED 3D REPRESENTATION AT SCALE |
d248157178 | Over the past few years, the acceleration of computing resources and research in deep learning has led to significant practical successes in a range of tasks, including in particular in computer vision. Building on these advances, reinforcement learning has also seen a leap forward with the emergence of agents capable ... | LOCAL FEATURE SWAPPING FOR GENERALIZATION IN REINFORCEMENT LEARNING |
d220280364 | Communication compression has been extensively adopted to speed up large-scale distributed optimization. However, most existing decentralized algorithms with compression are unsatisfactory in terms of convergence rate and stability. In this paper, we delineate two key obstacles in the algorithm design -data heterogenei... | Linear Convergent Decentralized Optimization with Compression |
d53192805 | Despite their impressive performance, deep neural networks exhibit striking failures on out-of-distribution inputs. One core idea of adversarial example research is to reveal neural network errors under such distribution shifts. We decompose these errors into two complementary sources: sensitivity and invariance. We sh... | EXCESSIVE INVARIANCE CAUSES ADVERSARIAL VULNERABILITY |
d263622509 | The recent success of Transformer in natural language processing has sparked its use in various domains.In offline reinforcement learning (RL), Decision Transformer (DT) is emerging as a promising model based on Transformer.However, we discovered that the attention module of DT is not appropriate to capture the inheren... | DECISION CONVFORMER: LOCAL FILTERING IN METAFORMER IS SUFFICIENT FOR DECISION MAKING |
d252918439 | Modern quantum annealers can find high-quality solutions to combinatorial optimisation objectives given as quadratic unconstrained binary optimisation (QUBO) problems. Unfortunately, obtaining suitable QUBO forms in computer vision remains challenging and currently requires problem-specific analytical derivations. More... | QuAnt: Quantum Annealing with Learnt Couplings |
d246823998 | Conditional contrastive learning frameworks consider the conditional sampling procedure that constructs positive or negative data pairs conditioned on specific variables. Fair contrastive learning constructs negative pairs, for example, from the same gender (conditioning on sensitive information), which in turn reduces... | CONDITIONAL CONTRASTIVE LEARNING WITH KERNEL |
d251953252 | We present a bi-encoder framework for named entity recognition (NER), which applies contrastive learning to map candidate text spans and entity types into the same vector representation space. Prior work predominantly approaches NER as sequence labeling or span classification. We instead frame NER as a representation l... | OPTIMIZING BI-ENCODER FOR NAMED ENTITY RECOGNITION VIA CONTRASTIVE LEARNING |
d238408406 | Learning accurate classifiers for novel categories from very few examples, known as few-shot image classification, is a challenging task in statistical machine learning and computer vision. The performance in few-shot classification suffers from the bias in the estimation of classifier parameters; however, an effective... | ON THE IMPORTANCE OF FIRTH BIAS REDUCTION IN FEW-SHOT CLASSIFICATION |
d199000713 | Many recent methods for unsupervised or self-supervised representation learning train feature extractors by maximizing an estimate of the mutual information (MI) between different views of the data. This comes with several immediate problems: For example, MI is notoriously hard to estimate, and using it as an objective... | ON MUTUAL INFORMATION MAXIMIZATION FOR REPRESENTATION LEARNING |
d221802286 | Recent work has explored the possibility of pruning neural networks at initialization. We assess proposals for doing so: SNIP (Lee et al., 2019), GraSP (Wang et al., 2020), SynFlow (Tanaka et al., 2020, and magnitude pruning. Although these methods surpass the trivial baseline of random pruning, they remain below the ... | PRUNING NEURAL NETWORKS AT INITIALIZATION: WHY ARE WE MISSING THE MARK? |
d198147763 | Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks, as well as for efficient scaling to large compute clusters. As current approaches suffer from limited bandwidth of the network, we propose the use of communication compression in the decentral... | Decentralized Deep Learning with Arbitrary Communication Compression |
d57189269 | Unsupervised image-to-image translation has gained considerable attention due to the recent impressive progress based on generative adversarial networks (GANs). However, previous methods often fail in challenging cases, in particular, when an image has multiple target instances and a translation task involves significa... | INSTAGAN: INSTANCE-AWARE IMAGE-TO-IMAGE TRANSLATION |
d259088588 | Lip-to-speech involves generating a natural-sounding speech synchronized with a soundless video of a person talking. Despite recent advances, current methods still cannot produce high-quality speech with high levels of intelligibility for challenging and realistic datasets such as LRS3. In this work, we present LipVoic... | LipVoicer: Generating Speech from Silent Videos Guided by Lip Reading |
d259298460 | We consider monotone variational inequality (VI) problems in multi-GPU settings where multiple processors/workers/clients have access to local stochastic dual vectors. This setting includes a broad range of important problems from distributed convex minimization to min-max and games. Extra-gradient, which is a de facto... | DISTRIBUTED EXTRA-GRADIENT WITH OPTIMAL COMPLEXITY AND COMMUNICATION GUARANTEES |
d218486818 | We show how neural models can be used to realize piece-wise constant functions such as decision trees. The proposed architecture, which we call locally constant networks, builds on ReLU networks that are piece-wise linear and hence their associated gradients with respect to the inputs are locally constant. We formally ... | OBLIQUE DECISION TREES FROM DERIVATIVES OF RELU NETWORKS |
d252781163 | Probabilistic Circuits (PCs) are a unified framework for tractable probabilistic models that support efficient computation of various probabilistic queries (e.g., marginal probabilities). One key challenge is to scale PCs to model large and highdimensional real-world datasets: we observe that as the number of parameter... | SCALING UP PROBABILISTIC CIRCUITS BY LATENT VARIABLE DISTILLATION |
d244714837 | Research on generalization bounds for deep networks seeks to give ways to predict test error using just the training dataset and the network parameters. While generalization bounds can give many insights about architecture design, training algorithms etc., what they do not currently do is yield good predictions for act... | On Predicting Generalization using GANs |
d261531159 | We present a new accelerated stochastic second-order method that is robust to both gradient and Hessian inexactness, which occurs typically in machine learning. We establish theoretical lower bounds and prove that our algorithm achieves optimal convergence in both gradient and Hessian inexactness in this key setting. W... | Advancing the lower bounds: An accelerated, stochastic, second-order method with optimal adaptation to inexactness |
d227305788 | Learning task-agnostic dynamics models in high-dimensional observation spaces can be challenging for model-based RL agents. We propose a novel way to learn latent world models by learning to predict sequences of future actions conditioned on task completion. These task-conditioned models adaptively focus modeling capac... | PLANNING FROM PIXELS USING INVERSE DYNAMICS MODELS |
d250407963 | In supervised learning, it has been shown that label noise in the data can be interpolated without penalties on test accuracy. We show that interpolating label noise induces adversarial vulnerability, and prove the first theorem showing the relationship between label noise and adversarial risk for any data distribution... | A LAW OF ADVERSARIAL RISK, INTERPOLATION, AND LABEL NOISE |
d88503668 | Despite the recent successes in robotic locomotion control, the design of robots, i.e., the design of their body structure, still heavily relies on human engineering. Automatic robot design has been a long studied subject, however, progress has been slow due to large combinatorial search space and the difficulty to eff... | NEURAL GRAPH EVOLUTION: TOWARDS EFFICIENT AUTOMATIC ROBOT DESIGN |
d222142276 | The dominant paradigm for learning video-text representations -noise contrastive learning -increases the similarity of the representations of pairs of samples that are known to be related, such as text and video from the same sample, and pushes away the representations of all other pairs. We posit that this last behavi... | Support-set bottlenecks for video-text representation learning |
d222133043 | We consider the problem of learning to communicate using multi-agent reinforcement learning (MARL). A common approach is to learn off-policy, using data sampled from a replay buffer. However, messages received in the past may not accurately reflect the current communication policy of each agent, and this complicates le... | CORRECTING EXPERIENCE REPLAY FOR MULTI-AGENT COMMUNICATION |
d245216646 | Musical expression requires control of both what notes are played, and how they are performed. Conventional audio synthesizers provide detailed expressive controls, but at the cost of realism. Black-box neural audio synthesis and concatenative samplers can produce realistic audio, but have few mechanisms for control. I... | MIDI-DDSP: DETAILED CONTROL OF MUSICAL PERFORMANCE VIA HIERARCHICAL MODELING |
d3508234 | We introduce a general method for improving the convergence rate of gradientbased optimizers that is easy to implement and works well in practice. We analyze the effectiveness of the method by applying it to stochastic gradient descent, stochastic gradient descent with Nesterov momentum, and Adam, showing that it impro... | Online Learning Rate Adaptation with Hypergradient Descent |
d208193111 | Adversarial training is one of the main defenses against adversarial attacks. In this paper, we provide the first rigorous study on diagnosing elements of large-scale adversarial training on ImageNet, which reveals two intriguing properties. First, we study the role of normalization. Batch Normalization (BN) is a cruci... | INTRIGUING PROPERTIES OF ADVERSARIAL TRAINING AT SCALE |
d251649165 | Graph Neural Networks (GNNs) are gaining extensive attention for their application in graph data. However, the black-box nature of GNNs prevents users from understanding and trusting the models, thus hampering their applicability. Whereas explaining GNNs remains a challenge, most existing methods fall into approximatio... | DEGREE: DECOMPOSITION BASED EXPLANATION FOR GRAPH NEURAL NETWORKS |
d1741724 | We study reinforcement learning of chatbots with recurrent neural network architectures when the rewards are noisy and expensive to obtain. For instance, a chatbot used in automated customer service support can be scored by quality assurance agents, but this process can be expensive, time consuming and noisy. Previous ... | BATCH POLICY GRADIENT METHODS FOR IMPROVING NEURAL CONVERSATION MODELS |
d238408001 | Vapnik-Chervonenkis (VC) theory has so far been unable to explain the small generalization error of overparametrized neural networks. Indeed, existing applications of VC theory to large networks obtain upper bounds on VC dimension that are proportional to the number of weights, and for a large class of networks, these ... | VC DIMENSION OF PARTIALLY QUANTIZED NEURAL NETWORKS IN THE OVERPARAMETRIZED REGIME |
d263909212 | The reasoning capabilities of LLM (Large Language Model) are widely acknowledged in recent research, inspiring studies on tool learning and autonomous agents. LLM serves as the "brain" of agent, orchestrating multiple tools for collaborative multi-step task solving. Unlike methods invoking tools like calculators or wea... | TOWARDS ROBUST MULTI-MODAL REASONING VIA MODEL SELECTION |
d202230734 | Fine-tuning a deep network trained with the standard cross-entropy loss is a strong baseline for few-shot learning. When fine-tuned transductively, this outperforms the current state-of-the-art on standard datasets such as Mini-Imagenet, Tiered-Imagenet, CIFAR-FS and FC-100 with the same hyper-parameters. The simplicit... | A BASELINE FOR FEW-SHOT IMAGE CLASSIFICATION |
d245334863 | We formally map the problem of sampling from an unknown distribution with a density in R d to the problem of learning and sampling a smoother density in R M d obtained by convolution with a fixed factorial kernel: the new density is referred to as M-density and the kernel as multimeasurement noise model (MNM). The M-de... | MULTIMEASUREMENT GENERATIVE MODELS |
d52891534 | The behavioral dynamics of multi-agent systems have a rich and orderly structure, which can be leveraged to understand these systems, and to improve how artificial agents learn to operate in them. Here we introduce Relational Forward Models (RFM) for multi-agent learning, networks that can learn to make accurate predic... | RELATIONAL FORWARD MODELS FOR MULTI-AGENT LEARNING |
d252222320 | Effective scaling and a flexible task interface enable large language models to excel at many tasks. We present PaLI (Pathways Language and Image model), a model that extends this approach to the joint modeling of language and vision. PaLI generates text based on visual and textual inputs, and with this interface perfo... | PALI: A JOINTLY-SCALED MULTILINGUAL LANGUAGE-IMAGE MODEL |
d247741535 | State-of-the-art deep Q-learning methods update Q-values using state transition tuples sampled from the experience replay buffer. This strategy often uniformly and randomly samples or prioritizes data sampling based on measures such as the temporal difference (TD) error. Such sampling strategies can be inefficient at l... | TOPOLOGICAL EXPERIENCE REPLAY |
d245335427 | We propose a framework to continuously learn object-centric representations for visual learning and understanding. Existing object-centric representations either rely on supervisions that individualize objects in the scene, or perform unsupervised disentanglement that can hardly deal with complex scenes in the real wor... | OBJECT PURSUIT: BUILDING A SPACE OF OBJECTS VIA DISCRIMINATIVE WEIGHT GENERATION |
d182952687 | Imitation learning allows agents to learn complex behaviors from demonstrations. However, learning a complex vision-based task may require an impractical number of demonstrations. Meta-imitation learning is a promising approach towards enabling agents to learn a new task from one or a few demonstrations by leveraging e... | WATCH, TRY, LEARN: META-LEARNING FROM DEMONSTRATIONS AND REWARDS |
d210839350 | We present Decentralized Distributed Proximal Policy Optimization (DD-PPO), a method for distributed reinforcement learning in resource-intensive simulated environments.DD-PPO is distributed (uses multiple machines), decentralized (lacks a centralized server), and synchronous (no computation is ever 'stale'), making it... | DD-PPO: LEARNING NEAR-PERFECT POINTGOAL NAVIGATORS FROM 2.5 BILLION FRAMES |
d3005102 | We introduce an exceptionally simple gated recurrent neural network (RNN) that achieves performance comparable to well-known gated architectures, such as LSTMs and GRUs, on the word-level language modeling task. We prove that our model has simple, predicable and non-chaotic dynamics. This stands in stark contrast to mo... | A RECURRENT NEURAL NETWORK WITHOUT CHAOS |
d256615740 | Learning on graphs, where instance nodes are inter-connected, has become one of the central problems for deep learning, as relational structures are pervasive and induce data inter-dependence which hinders trivial adaptation of existing approaches that assume inputs to be i.i.d. sampled. However, current models mostly ... | ENERGY-BASED OUT-OF-DISTRIBUTION DETECTION FOR GRAPH NEURAL NETWORKS |
d224705447 | We propose methods to strengthen the invariance properties of representations obtained by contrastive learning. While existing approaches implicitly induce a degree of invariance as representations are learned, we look to more directly enforce invariance in the encoding process. To this end, we first introduce a traini... | IMPROVING TRANSFORMATION INVARIANCE IN CONTRASTIVE REPRESENTATION LEARNING |
d231632806 | We define a notion of information that an individual sample provides to the training of a neural network, and we specialize it to measure both how much a sample informs the final weights and how much it informs the function computed by the weights. Though related, we show that these quantities have a qualitatively diff... | ESTIMATING INFORMATIVENESS OF SAMPLES WITH SMOOTH UNIQUE INFORMATION |
d52954637 | The smallest eigenvectors of the graph Laplacian are well-known to provide a succinct representation of the geometry of a weighted graph. In reinforcement learning (RL), where the weighted graph may be interpreted as the state transition process induced by a behavior policy acting on the environment, approximating the ... | The Laplacian in RL: Learning Representations with Efficient Approximations |
d252600018 | Recent works have widely adopted large language model pretraining for source code, suggested source code-specific pretraining objectives and investigated the applicability of various Transformer-based language model architectures for source code. This work investigates another important aspect of such models, namely th... | CODEBPE: INVESTIGATING SUBTOKENIZATION OPTIONS FOR LARGE LANGUAGE MODEL PRETRAINING ON SOURCE CODE |
d52894096 | Recurrent neural networks (RNNs) have shown excellent performance in processing sequence data. However, they are both complex and memory intensive due to their recursive nature. These limitations make RNNs difficult to embed on mobile devices requiring real-time processes with limited hardware resources. To address the... | LEARNING RECURRENT BINARY/TERNARY WEIGHTS |
d233740346 | We study how representation learning can improve the efficiency of bandit problems. We study the setting where we play T linear bandits with dimension d concurrently, and these T bandit tasks share a common k(≪ d) dimensional linear representation. For the finite-action setting, we present a new algorithm whichwhere N ... | IMPACT OF REPRESENTATION LEARNING IN LINEAR BANDITS |
d49303347 | Learning multimodal representations is a fundamentally complex research problem due to the presence of multiple heterogeneous sources of information. Although the presence of multiple modalities provides additional valuable information, there are two key challenges to address when learning from multimodal data: 1) mode... | LEARNING FACTORIZED MULTIMODAL REPRESENTATIONS |
d256105170 | Real-world data generation often involves complex inter-dependencies among instances, violating the IID-data hypothesis of standard learning paradigms and posing a challenge for uncovering the geometric structures for learning desired instance representations. To this end, we introduce an energy constrained diffusion m... | DIFFORMER: SCALABLE (GRAPH) TRANSFORMERS INDUCED BY ENERGY CONSTRAINED DIFFUSION |
d264439585 | Privacy amplification exploits randomness in data selection to provide tighter differential privacy (DP) guarantees.This analysis is key to DP-SGD's success in machine learning (ML), but, is not readily applicable to the newer state-of-the-art (SOTA) algorithms.This is because these algorithms, known as DP-FTRL, use th... | Privacy Amplification for Matrix Mechanisms |
d12462234 | The capacity of a neural network to absorb information is limited by its number of parameters. Conditional computation, where parts of the network are active on a per-example basis, has been proposed in theory as a way of dramatically increasing model capacity without a proportional increase in computation. In practice... | OUTRAGEOUSLY LARGE NEURAL NETWORKS: THE SPARSELY-GATED MIXTURE-OF-EXPERTS LAYER |
d204207649 | Transferring knowledge across tasks to improve data-efficiency is one of the open key challenges in the field of global black-box optimization. Readily available algorithms are typically designed to be universal optimizers and, therefore, often suboptimal for specific tasks. We propose a novel transfer learning method ... | META-LEARNING ACQUISITION FUNCTIONS FOR TRANSFER LEARNING IN BAYESIAN OPTIMIZATION |
d263334596 | Molecular Representation Learning (MRL) has proven impactful in numerous biochemical applications such as drug discovery and enzyme design.While Graph Neural Networks (GNNs) are effective at learning molecular representations from a 2D molecular graph or a single 3D structure, existing works often overlook the flexible... | Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks |
d248986239 | Chain-of-thought prompting has demonstrated remarkable performance on various natural language reasoning tasks. However, it tends to perform poorly on tasks which requires solving problems harder than the exemplars shown in the prompts. To overcome this challenge of easy-to-hard generalization, we propose a novel promp... | LEAST-TO-MOST PROMPTING ENABLES COMPLEX REASONING IN LARGE LANGUAGE MODELS |
d220265992 | Kernelized Stein discrepancy (KSD), though being extensively used in goodness-offit tests and model learning, suffers from the curse-of-dimensionality. We address this issue by proposing the sliced Stein discrepancy and its scalable and kernelized variants, which employs kernel-based test functions defined on the optim... | Sliced Kernelized Stein Discrepancy |
d56895597 | Invariant and equivariant networks have been successfully used for learning images, sets, point clouds, and graphs. A basic challenge in developing such networks is finding the maximal collection of invariant and equivariant linear layers. Although this question is answered for the first three examples (for popular tra... | INVARIANT AND EQUIVARIANT GRAPH NETWORKS |
d3544583 | The rapid adoption of machine learning has increased concerns about the privacy implications of machine learning models trained on sensitive data, such as medical records or other personal information. To address those concerns, one promising approach is Private Aggregation of Teacher Ensembles, or PATE, which transfer... | SCALABLE PRIVATE LEARNING WITH PATE |
d232170254 | Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy based on offline data previously collected under different policies. Therefore, OPE is a key step in applying reinforcement learning to real-world domains such as medical treatment, where interactive data collection is expensive... | Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds |
d254685748 | Boundary conditions (BCs) are important groups of physics-enforced constraints that are necessary for solutions of Partial Differential Equations (PDEs) to satisfy at specific spatial locations. These constraints carry important physical meaning, and guarantee the existence and the uniqueness of the PDE solution. Curre... | Guiding continuous operator learning through Physics-based boundary constraints |
d254018206 | We present a novel neural network architecture using self-attention, the Wavefunction Transformer (Psiformer), which can be used as an approximation (or Ansatz) for solving the many-electron Schrödinger equation, the fundamental equation for quantum chemistry and material science. This equation can be solved from first... | A Self-Attention Ansatz for Ab-initio Quantum Chemistry |
d53415112 | Reinforcement learning agents need exploratory behaviors to escape from local optima. These behaviors may include both immediate dithering perturbation and temporally consistent exploration. To achieve these, a stochastic policy model that is inherently consistent through a period of time is in desire, especially for t... | NADPEX: AN ON-POLICY TEMPORALLY CONSISTENT EXPLORATION METHOD FOR DEEP REINFORCEMENT LEARNING |
d246430145 | Learning multiple tasks sequentially without forgetting previous knowledge, called Continual Learning (CL), remains a long-standing challenge for neural networks. Most existing methods rely on additional network capacity or data replay. In contrast, we introduce a novel approach which we refer to as Recursive Gradient ... | CONTINUAL LEARNING WITH RECURSIVE GRADIENT OPTIMIZATION |
d247011095 | Human intervention is an effective way to inject human knowledge into the loop of reinforcement learning, bringing fast learning and training safety. But given the very limited budget of human intervention, it is challenging to design when and how human expert interacts with the learning agent in the training. In this ... | EFFICIENT LEARNING OF SAFE DRIVING POLICY VIA HUMAN-AI COPILOT OPTIMIZATION |
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