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d252683312 | Grokking, the unusual phenomenon for algorithmic datasets where generalization happens long after overfitting the training data, has remained elusive. We aim to understand grokking by analyzing the loss landscapes of neural networks, identifying the mismatch between training and test loss landscapes as the cause for gr... | OMNIGROK: GROKKING BEYOND ALGORITHMIC DATA |
d53438249 | Learning policies on data synthesized by models can in principle quench the thirst of reinforcement learning algorithms for large amounts of real experience, which is often costly to acquire. However, simulating plausible experience de novo is a hard problem for many complex environments, often resulting in biases for ... | WOULDA, COULDA, SHOULDA: COUNTERFACTUALLY-GUIDED POLICY SEARCH |
d243756979 | Self-supervised learning provides a promising path towards eliminating the need for costly label information in representation learning on graphs. However, to achieve state-of-the-art performance, methods often need large numbers of negative examples and rely on complex augmentations. This can be prohibitively expensiv... | LARGE-SCALE REPRESENTATION LEARNING ON GRAPHS VIA BOOTSTRAPPING |
d3508638 | Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as powerful frameworks for deep generative model learning, have largely been considered as two distinct paradigms and received extensive independent study respectively. Th... | On Unifying Deep Generative Models |
d235614375 | Federated learning (FL) allows edge devices to collectively learn a model without directly sharing data within each device, thus preserving privacy and eliminating the need to store data globally. While there are promising results under the assumption of independent and identically distributed (iid) local data, current... | FEDMIX: APPROXIMATION OF MIXUP UNDER MEAN AUGMENTED FEDERATED LEARNING |
d220041972 | For many tasks, the reward function is too complex to be specified procedurally, and must instead be learned from user data. Prior work has evaluated learned reward functions by examining rollouts from a policy optimized for the learned reward. However, this method cannot distinguish between the learned reward function... | Quantifying Differences in Reward Functions |
d252846202 | Learning energy-based models (EBMs) is known to be difficult especially on discrete data where gradient-based learning strategies cannot be applied directly. Although ratio matching is a sound method to learn discrete EBMs, it suffers from expensive computation and excessive memory requirements, thereby resulting in di... | GRADIENT-GUIDED IMPORTANCE SAMPLING FOR LEARNING BINARY ENERGY-BASED MODELS |
d6104263 | We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process. The generation network maps samples from stochastic latent variables to the data space while the inference network maps training examples in data space to the sp... | Adversarially Learned Inference |
d4564356 | Recurrent neural networks (RNN), convolutional neural networks (CNN) and selfattention networks (SAN) are commonly used to produce context-aware representations. RNN can capture long-range dependency but is hard to parallelize and not time-efficient. CNN focuses on local dependency but does not perform well on some tas... | BI-DIRECTIONAL BLOCK SELF-ATTENTION FOR FAST AND MEMORY-EFFICIENT SEQUENCE MODELING |
d247518628 | We study the problem of aligning the supports of distributions. Compared to the existing work on distribution alignment, support alignment does not require the densities to be matched. We propose symmetric support difference as a divergence measure to quantify the mismatch between supports. We show that select discrimi... | ADVERSARIAL SUPPORT ALIGNMENT |
d238419023 | In this paper, we propose a novel neural exploration strategy in contextual bandits, EE-Net, distinct from the standard UCB-based and TS-based approaches. Contextual multi-armed bandits have been studied for decades with various applications. To solve the exploitation-exploration tradeoff in bandits, there are three ma... | EE-NET: EXPLOITATION-EXPLORATION NEURAL NETWORKS IN CONTEXTUAL BANDITS |
d244478674 | Despite the recent success of multi-task learning and transfer learning for natural language processing (NLP), few works have systematically studied the effect of scaling up the number of tasks during pre-training. Towards this goal, this paper introduces EXMIX (Extreme Mixture): a massive collection of 107 supervised ... | EXT5: TOWARDS EXTREME MULTI-TASK SCALING FOR TRANSFER LEARNING |
d258762594 | Deep graph clustering has recently received significant attention due to its ability to enhance the representation learning capabilities of models in unsupervised scenarios. Nevertheless, deep clustering for temporal graphs, which could capture crucial dynamic interaction information, has not been fully explored. It me... | Deep Temporal Graph Clustering |
d221112385 | Recent self-supervised contrastive methods have been able to produce impressive transferable visual representations by learning to be invariant to different data augmentations. However, these methods implicitly assume a particular set of representational invariances (e.g., invariance to color), and can perform poorly w... | What Should Not Be Contrastive in Contrastive Learning |
d67855984 | Learning with a primary objective, such as softmax cross entropy for classification and sequence generation, has been the norm for training deep neural networks for years. Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class f... | COMPLEMENT OBJECTIVE TRAINING |
d10635893 | Data noising is an effective technique for regularizing neural network models. While noising is widely adopted in application domains such as vision and speech, commonly used noising primitives have not been developed for discrete sequencelevel settings such as language modeling. In this paper, we derive a connection b... | DATA NOISING AS SMOOTHING IN NEURAL NETWORK LANGUAGE MODELS |
d250144560 | Recent work on deep learning for tabular data demonstrates the strong performance of deep tabular models, often bridging the gap between gradient boosted decision trees and neural networks. Accuracy aside, a major advantage of neural models is that they are easily fine-tuned in new domains and learn reusable features. ... | TRANSFER LEARNING WITH DEEP TABULAR MODELS |
d258291930 | The recent GPT-4 has demonstrated extraordinary multi-modal abilities, such as directly generating websites from handwritten text and identifying humorous elements within images.These features are rarely observed in previous visionlanguage models.However, the technical details behind GPT-4 continue to remain undisclose... | MINIGPT-4: ENHANCING VISION-LANGUAGE UNDERSTANDING WITH ADVANCED LARGE LANGUAGE MODELS |
d252596302 | How can we design protein sequences folding into the desired structures effectively and efficiently? AI methods for structure-based protein design have attracted increasing attention in recent years; however, few methods can simultaneously improve the accuracy and efficiency due to the lack of expressive features and a... | PIFOLD: TOWARD EFFECTIVE AND EFFICIENT PROTEIN INVERSE FOLDING |
d195218755 | Model-based reinforcement learning (MBRL) with model-predictive control or online planning has shown great potential for locomotion control tasks in terms of both sample efficiency and asymptotic performance. Despite their initial successes, the existing planning methods search from candidate sequences randomly generat... | Exploring Model-based Planning with Policy Networks |
d244488409 | Despite extensive progress on image generation, common deep generative model architectures are not easily applied to lossless compression. For example, VAEs suffer from a compression cost overhead due to their latent variables. This overhead can only be partially eliminated with elaborate schemes such as bits-back codi... | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
d252846354 | Understanding dynamics from visual observations is a challenging problem that requires disentangling individual objects from the scene and learning their interactions. While recent object-centric models can successfully decompose a scene into objects, modeling their dynamics effectively still remains a challenge. We ad... | SLOTFORMER: UNSUPERVISED VISUAL DYNAMICS SIMULATION WITH OBJECT-CENTRIC MODELS |
d54558282 | It is important to detect anomalous inputs when deploying machine learning systems. The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples. At the same time, diverse image and text data are available in enormous quantities. We... | DEEP ANOMALY DETECTION WITH OUTLIER EXPOSURE |
d222134093 | In today's heavily overparameterized models, the value of the training loss provides few guarantees on model generalization ability. Indeed, optimizing only the training loss value, as is commonly done, can easily lead to suboptimal model quality. Motivated by prior work connecting the geometry of the loss landscape an... | SHARPNESS-AWARE MINIMIZATION FOR EFFICIENTLY IMPROVING GENERALIZATION |
d244773609 | Overparameterized neural networks generalize well but are expensive to train. Ideally, one would like to reduce their computational cost while retaining their generalization benefits. Sparse model training is a simple and promising approach to achieve this, but there remain challenges as existing methods struggle with ... | Pixelated Butterfly: Simple and Efficient Sparse Training for Neural Network Models |
d258987659 | In recent years, large language models have greatly improved in their ability to perform complex multi-step reasoning. However, even stateof-the-art models still regularly produce logical mistakes. To train more reliable models, we can turn either to outcome supervision, which provides feedback for a final result, or p... | Let's Verify Step by Step |
d829159 | Attention plays a critical role in human visual experience. Furthermore, it has recently been demonstrated that attention can also play an important role in the context of applying artificial neural networks to a variety of tasks from fields such as computer vision and NLP. In this work we show that, by properly defini... | PAYING MORE ATTENTION TO ATTENTION: IMPROVING THE PERFORMANCE OF CONVOLUTIONAL NEURAL NETWORKS VIA ATTENTION TRANSFER |
d237263489 | Meta-reinforcement learning (meta-RL) algorithms allow for agents to learn new behaviors from small amounts of experience, mitigating the sample inefficiency problem in RL. However, while meta-RL agents can adapt quickly to new tasks at test time after experiencing only a few trajectories, the meta-training process is ... | HINDSIGHT FORESIGHT RELABELING FOR META-REINFORCEMENT LEARNING |
d220831336 | We propose Multi-Level Local SGD, a distributed stochastic gradient method for learning a smooth, non-convex objective in a multi-level communication network with heterogeneous workers. Our network model consists of a set of disjoint subnetworks, with a single hub and multiple workers; further, workers may have differe... | MULTI-LEVEL LOCAL SGD: DISTRIBUTED SGD FOR HETEROGENEOUS HIERARCHICAL NETWORKS |
d209832300 | Reinforcement learning (RL) combines a control problem with statistical estimation: the system dynamics are not known to the agent, but can be learned through experience. A recent line of research casts 'RL as inference' and suggests a particular framework to generalize the RL problem as probabilistic inference. Our pa... | MAKING SENSE OF REINFORCEMENT LEARNING AND PROBABILISTIC INFERENCE |
d238744233 | Training deep neural networks is a challenging non-convex optimization problem. Recent work has proven that the strong duality holds (which means zero duality gap) for regularized finite-width two-layer ReLU networks and consequently provided an equivalent convex training problem. However, extending this result to deep... | PARALLEL DEEP NEURAL NETWORKS HAVE ZERO DUALITY GAP |
d53116042 | We give a new algorithm for learning a two-layer neural network under a general class of input distributions. Assuming there is a ground-truth two-layer networkwhere A, W are weight matrices, ξ represents noise, and the number of neurons in the hidden layer is no larger than the input or output, our algorithm is guaran... | Learning Two-layer Neural Networks with Symmetric Inputs |
d247595263 | Chain-of-thought prompting combined with pre-trained large language models has achieved encouraging results on complex reasoning tasks. In this paper, we propose a new decoding strategy, self-consistency, to replace the naive greedy decoding used in chain-of-thought prompting. It first samples a diverse set of reasonin... | SELF-CONSISTENCY IMPROVES CHAIN OF THOUGHT REASONING IN LANGUAGE MODELS |
d247476224 | Federated learning (FL) aims to minimize the communication complexity of training a model over heterogeneous data distributed across many clients. A common approach is local update methods, where clients take multiple optimization steps over local data before communicating with the server (e.g., FedAvg). Local update m... | FEDCHAIN: CHAINED ALGORITHMS FOR NEAR-OPTIMAL COMMUNICATION COST IN FEDERATED LEARNING |
d56895473 | Counterfactual regret minimization (CFR) is the most popular algorithm on solving two-player zero-sum extensive games with imperfect information and achieves state-of-the-art results in practice. However, the performance of CFR is not fully understood, since empirical results on the regret are much better than the know... | Lazy-CFR: fast and near-optimal regret minimization for extensive games with imperfect information |
d264406064 | The wide-ranging applications of large language models (LLMs), especially in safety-critical domains, necessitate the proper evaluation of the LLM's adversarial robustness.This paper proposes an efficient tool to audit the LLM's adversarial robustness via a prompt-based adversarial attack (PromptAttack).PromptAttack co... | AN LLM CAN FOOL ITSELF: A PROMPT-BASED ADVERSARIAL ATTACK |
d259129486 | We present PeFLL, a new personalized federated learning algorithm that improves over the state-of-the-art in three aspects: 1) it produces more accurate models, especially in the low-data regime, and not only for clients present during its training phase, but also for any that may emerge in the future; 2) it reduces th... | PEFLL: PERSONALIZED FEDERATED LEARNING BY LEARNING TO LEARN |
d258436870 | In-context learning (ICL) is an important capability of Large Language Models (LLMs), enabling these models to dynamically adapt based on specific, in-context exemplars, thereby improving accuracy and relevance. However, LLM's responses may leak the sensitive private information contained in in-context exemplars. To ad... | PRIVACY-PRESERVING IN-CONTEXT LEARNING FOR LARGE LANGUAGE MODELS |
d67856605 | Generative adversarial networks (GANs) have been shown to provide an effective way to model complex distributions and have obtained impressive results on various challenging tasks. However, typical GANs require fully-observed data during training. In this paper, we present a GAN-based framework for learning from comple... | MISGAN: LEARNING FROM INCOMPLETE DATA WITH GENERATIVE ADVERSARIAL NETWORKS |
d253265114 | Neural language models (LMs) have achieved impressive results on various language-based reasoning tasks by utilizing latent knowledge encoded in their own pretrained parameters. To make this reasoning process more explicit, recent works retrieve a rationalizing LM's internal knowledge by training or prompting it to gen... | PINTO: FAITHFUL LANGUAGE REASONING USING PROMPT-GENERATED RATIONALES |
d248377334 | Neural networks can be trained to solve partial differential equations (PDEs) by using the PDE residual as the loss function. This strategy is called "physicsinformed neural networks" (PINNs), but it currently cannot produce high-accuracy solutions, typically attaining about 0.1% relative error. We present an adversari... | COMPETITIVE PHYSICS INFORMED NETWORKS |
d264487387 | Deep-learning models can extract a rich assortment of features from data.Which features a model uses depends not only on predictivity-how reliably a feature indicates train-set labels-but also on availability-how easily the feature can be extracted, or leveraged, from inputs.The literature on shortcut learning has note... | On the Foundations of Shortcut Learning |
d257219875 | Active domain adaptation (DA) aims to maximally boost the model adaptation on a new target domain by actively selecting limited target data to annotate, whereas traditional active learning methods may be less effective since they do not consider the domain shift issue. Despite active DA methods address this by further ... | DIRICHLET-BASED UNCERTAINTY CALIBRATION FOR ACTIVE DOMAIN ADAPTATION |
d263310960 | Generalization to out-of-distribution (OOD) data is a critical challenge in machine learning. Ensemble-based methods, like weight space ensembles that interpolate model parameters, have been shown to achieve superior OOD performance. However, the underlying mechanism for their effectiveness remains unclear.In this stud... | Spurious Feature Diversification Improves Out-of-distribution Generalization |
d247318577 | Parallelizing Gated Recurrent Unit (GRU) networks is a challenging task, as the training procedure of GRU is inherently sequential. Prior efforts to parallelize GRU have largely focused on conventional parallelization strategies such as dataparallel and model-parallel training algorithms. However, when the given sequen... | PARALLEL TRAINING OF GRU NETWORKS WITH A MULTI-GRID SOLVER FOR LONG SEQUENCES |
d6775391 | We describe an image compression method, consisting of a nonlinear analysis transformation, a uniform quantizer, and a nonlinear synthesis transformation. The transforms are constructed in three successive stages of convolutional linear filters and nonlinear activation functions. Unlike most convolutional neural networ... | END-TO-END OPTIMIZED IMAGE COMPRESSION |
d239049633 | We show that the simplest actor-critic method -a linear softmax policy updated with TD through interaction with a linear MDP, but featuring no explicit regularization or explorationdoes not merely find an optimal policy, but moreover prefers high entropy optimal policies. To demonstrate the strength of this bias, the a... | Actor-critic is implicitly biased towards high entropy optimal policies |
d229371407 | Synthesizing programs from examples requires searching over a vast, combinatorial space of possible programs. In this search process, a key challenge is representing the behavior of a partially written program before it can be executed, to judge if it is on the right track and predict where to search next. We introduce... | REPRESENTING PARTIAL PROGRAMS WITH BLENDED ABSTRACT SEMANTICS |
d224803601 | In open question answering (QA), the answer to a question is produced by retrieving and then analyzing documents that might contain answers to the question. Most open QA systems have considered only retrieving information from unstructured text. Here we consider for the first time open QA over both tabular and textual ... | OPEN QUESTION ANSWERING OVER TABLES AND TEXT |
d210063976 | The use of deep pre-trained transformers has led to remarkable progress in a number of applications . For tasks that make pairwise comparisons between sequences, matching a given input with a corresponding label, two approaches are common: Cross-encoders performing full self-attention over the pair and Bi-encoders enco... | Poly-encoders: architectures and pre-training strategies for fast and accurate multi-sentence scoring |
d262084051 | Large language models (LLMs) have pushed the limits of natural language understanding and exhibited excellent problem-solving ability.Despite the great success, most existing open-source LLMs (e.g., LLaMA-2) are still far away from satisfactory for solving mathematical problems due to the complex reasoning procedures.T... | METAMATH: BOOTSTRAP YOUR OWN MATHEMATICAL QUESTIONS FOR LARGE LANGUAGE MODELS |
d249209899 | Part-prototype Networks (ProtoPNets) are concept-based classifiers designed to achieve the same performance as black-box models without compromising transparency. ProtoPNets compute predictions based on similarity to class-specific part-prototypes learned to recognize parts of training examples, making it easy to faith... | CONCEPT-LEVEL DEBUGGING OF PART-PROTOTYPE NETWORKS |
d260378901 | Figure 1: Generated image of size 1024×512 using the model trained on 21k natural images using a 148M-parameters model.AbstractWe propose an effective denoising diffusion model for generating high-resolution images (e.g., 1024×512), trained on small-size image patches (e.g., 64×64). We name our algorithm Patch-DM, in w... | Patched Denoising Diffusion Models For High-Resolution Image Synthesis |
d238407710 | Graph convolutional networks (GCNs) and their variants have achieved great success in dealing with graph-structured data. Nevertheless, it is well known that deep GCNs suffer from the over-smoothing problem, where node representations tend to be indistinguishable as more layers are stacked up. The theoretical research ... | TOWARDS DEEPENING GRAPH NEURAL NETWORKS: A GNTK-BASED OPTIMIZATION PERSPECTIVE |
d244345628 | We present a method to compute the derivative of a learning task with respect to a dataset. A learning task is a function from a training set to the validation error, which can be represented by a trained deep neural network (DNN). The "dataset derivative" is a linear operator, computed around the trained model, that i... | DIVA: Dataset Derivative of a Learning Task |
d208547770 | Neural networks have a reputation for being better at solving statistical or approximate problems than at performing calculations or working with symbolic data. In this paper, we show that they can be surprisingly good at more elaborated tasks in mathematics, such as symbolic integration and solving differential equati... | DEEP LEARNING FOR SYMBOLIC MATHEMATICS |
d257205760 | We prove that the set of functions representable by ReLU neural networks with integer weights strictly increases with the network depth while allowing arbitrary width. More precisely, we show that ⌈log 2 (n)⌉ hidden layers are indeed necessary to compute the maximum of n numbers, matching known upper bounds. Our result... | LOWER BOUNDS ON THE DEPTH OF INTEGRAL RELU NEURAL NETWORKS VIA LATTICE POLYTOPES |
d204922497 | We present a variational approximation to the information bottleneck ofTishby et al. (1999). This variational approach allows us to parameterize the information bottleneck model using a neural network and leverage the reparameterization trick for efficient training. We call this method "Deep Variational Information Bot... | DEEP VARIATIONAL INFORMATION BOTTLENECK |
d189898036 | Recent works on implicit regularization have shown that gradient descent converges to the max-margin direction for logistic regression with one-layer or multi-layer linear networks. In this paper, we generalize this result to homogeneous neural networks, including fully-connected and convolutional neural networks with ... | Gradient Descent Maximizes the Margin of Homogeneous Neural Networks |
d252595791 | Existing federated learning paradigms usually extensively exchange distributed models at a central solver to achieve a more powerful model. However, this would incur severe communication burden between a server and multiple clients especially when data distributions are heterogeneous. As a result, current federated lea... | META KNOWLEDGE CONDENSATION FOR FEDERATED LEARNING |
d49882757 | In this work, we propose an alternative solution for parallel wave generation by WaveNet. In contrast to parallel WaveNet (Oord et al., 2018), we distill a Gaussian inverse autoregressive flow from the autoregressive WaveNet by minimizing a novel regularized KL divergence between their highly-peaked output distribution... | ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech |
d231592453 | This paper is concerned with self-supervised learning for small models. The problem is motivated by our empirical studies that while the widely used contrastive self-supervised learning method has shown great progress on large model training, it does not work well for small models. To address this problem, we propose a... | SEED: SELF-SUPERVISED DISTILLATION FOR VISUAL REPRESENTATION |
d204893960 | Generalization of deep networks has been of great interest in recent years, resulting in a number of theoretically and empirically motivated complexity measures. However, most papers proposing such measures study only a small set of models, leaving open the question of whether the conclusion drawn from those experiment... | Fantastic Generalization Measures and Where to Find Them |
d211988986 | Recent empirical and theoretical studies have shown that many learning algorithms -from linear regression to neural networks -can have test performance that is non-monotonic in quantities such the sample size and model size. This striking phenomenon, often referred to as "double descent", has raised questions of if we ... | Optimal Regularization Can Mitigate Double Descent |
d227247851 | State-of-the-art natural language processing (NLP) models often learn to model dataset biases and surface form correlations instead of features that target the intended underlying task. Previous work has demonstrated effective methods to circumvent these issues when knowledge of the bias is available. We consider cases... | LEARNING FROM OTHERS' MISTAKES: AVOIDING DATASET BIASES WITHOUT MODELING THEM |
d235485300 | An important challenge facing modern machine learning is how to rigorously quantify the uncertainty of model predictions. Conveying uncertainty is especially important when there are changes to the underlying data distribution that might invalidate the predictive model. Yet, most existing uncertainty quantification alg... | PAC PREDICTION SETS UNDER COVARIATE SHIFT |
d222208678 | Set prediction is about learning to predict a collection of unordered variables with unknown interrelations. Training such models with set losses imposes the structure of a metric space over sets. We focus on stochastic and underdefined cases, where an incorrectly chosen loss function leads to implausible predictions. ... | SET PREDICTION WITHOUT IMPOSING STRUCTURE AS CONDITIONAL DENSITY ESTIMATION |
d251223792 | Widely used evaluation metrics for text generation either do not work well with longer texts or fail to evaluate all aspects of text quality. In this paper, we introduce a new metric called SMART to mitigate such limitations. Specifically, We treat sentences as basic units of matching instead of tokens, and use a sente... | SMART: Sentences as Basic Units for Text Evaluation |
d252595995 | Partial Observability-where agents can only observe partial information about the true underlying state of the system-is ubiquitous in real-world applications of Reinforcement Learning (RL). Theoretically, learning a near-optimal policy under partial observability is known to be hard in the worst case due to an exponen... | Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms |
d252917984 | Convolutional models have been widely used in multiple domains. However, most existing models only use local convolution, making the model unable to handle long-range dependency efficiently. Attention overcomes this problem by aggregating global information based on the pair-wise attention score but also makes the comp... | What Makes Convolutional Models Great on Long Sequence Modeling? |
d239050360 | While large pre-trained models have enabled impressive results on a variety of downstream tasks, the largest existing models still make errors, and even accurate predictions may become outdated over time. Because detecting all such failures at training time is impossible, enabling both developers and end users of such ... | FAST MODEL EDITING AT SCALE |
d263608308 | Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input.However, they operate on a fixed input graph structure, ignoring potential noise and missing information.Furthermore, their local aggregation mechanism can lead to problems such as over-squashing and limited ex... | PROBABILISTICALLY REWIRED MESSAGE-PASSING NEURAL NETWORKS |
d239616082 | GENEDISCO: A BENCHMARK FOR EXPERIMENTAL DESIGN IN DRUG DISCOVERY | |
d252545164 | A proper parametrization of state transition matrices of linear state-space models (SSMs) followed by standard nonlinearities enables them to efficiently learn representations from sequential data, establishing the state-ofthe-art on a large series of long-range sequence modeling benchmarks. In this paper, we show that... | Liquid Structural State-Space Models |
d59842932 | The human ability to recognize objects is impaired when the object is not shown in full. "Minimal images" are the smallest regions of an image that remain recognizable for humans.Ullman et al. (2016)show that a slight modification of the location and size of the visible region of the minimal image produces a sharp drop... | MINIMAL IMAGES IN DEEP NEURAL NETWORKS: FRAGILE OBJECT RECOGNITION IN NATURAL IMAGES |
d239049483 | End-to-end (geometric) deep learning has seen first successes in approximating the solution of combinatorial optimization problems. However, generating data in the realm of NP-hard/-complete tasks brings practical and theoretical challenges, resulting in evaluation protocols that are too optimistic. Specifically, most ... | GENERALIZATION OF NEURAL COMBINATORIAL SOLVERS THROUGH THE LENS OF ADVERSARIAL ROBUSTNESS |
d267782625 | The static synaptic connectivity of neuronal circuits stands in direct contrast to the dynamics of their function.As in changing community interactions, different neurons can participate actively in various combinations to effect behaviors at different times.We introduce an unsupervised approach to learn the dynamic af... | LEARNING DYNAMIC REPRESENTATIONS OF THE FUNCTIONAL CONNECTOME IN NEUROBIOLOGICAL NETWORKS |
d264146270 | Vertical Federated Learning (VFL) has emerged as a collaborative training paradigm that allows participants with different features of the same group of users to accomplish cooperative training without exposing their raw data or model parameters.VFL has gained significant attention for its research potential and real-w... | VFLAIR: A Research Library and Benchmark for Vertical Federated Learning |
d18362887 | Sum-product networks have recently emerged as an attractive representation due to their dual view as a special type of deep neural network with clear semantics and a special type of probabilistic graphical model for which inference is always tractable. Those properties follow from some conditions (i.e., completeness an... | Online Structure Learning for Sum-Product Networks with Gaussian Leaves |
d4722462 | While most machine translation systems to date are trained on large parallel corpora, humans learn language in a different way: by being grounded in an environment and interacting with other humans. In this work, we propose a communication game where two agents, native speakers of their own respective languages, jointl... | EMERGENT TRANSLATION IN MULTI-AGENT COMMUNICATION |
d49881601 | We study the problem of learning similarity functions over very large corpora using neural network embedding models. These models are typically trained using SGD with sampling of random observed and unobserved pairs, with a number of samples that grows quadratically with the corpus size, making it expensive to scale to... | Efficient Training on Very Large Corpora via Gramian Estimation |
d221376730 | Reinforcement learning (RL) in episodic factored Markov decision processes (FMDPs) is studied. We propose an algorithm called FMDP-BF, which leverages the factorization structure of the FMDP. The algorithm's regret is shown to be exponentially smaller than optimal algorithms in non-factored MDPs, and improves on the be... | Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL |
d246652474 | Existing domain adaptation methods tend to treat every domain equally and align them all perfectly. Such uniform alignment ignores topological structures among different domains; therefore it may be beneficial for nearby domains, but not necessarily for distant domains. In this work, we relax such uniform alignment by ... | GRAPH-RELATIONAL DOMAIN ADAPTATION |
d259075246 | Large Language Models (LLMs) have greatly advanced code auto-completion systems, with a potential for substantial productivity enhancements for developers.However, current benchmarks mainly focus on single-file tasks, leaving an assessment gap for more complex, real-world, multi-file programming scenarios.To fill this ... | RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems |
d247996981 | We introduce LilNetX, an end-to-end trainable technique for neural networks that enables learning models with specified accuracy-rate-computation trade-off. Prior works approach these problems one at a time and often require post-processing or multistage training which become less practical and do not scale very well f... | LilNetX: Lightweight Networks with EXtreme Model Compression and Structured Sparsification |
d222272074 | A common approach for compressing NLP networks is to encode the embedding layer as a matrix A ∈ R n×d , compute its rank-j approximation A j via SVD, and then factor A j into a pair of matrices that correspond to smaller fully-connected layers to replace the original embedding layer. Geometrically, the rows of A repres... | DEEP LEARNING MEETS PROJECTIVE CLUSTERING |
d3334304 | Deep reinforcement learning has achieved many recent successes, but our understanding of its strengths and limitations is hampered by the lack of rich environments in which we can fully characterize optimal behavior, and correspondingly diagnose individual actions against such a characterization. Here we consider a fam... | CAN DEEP REINFORCEMENT LEARNING SOLVE ERDOS-SELFRIDGE-SPENCER GAMES? |
d264439160 | Large language models exhibit surprising emergent generalization properties, yet also struggle on many simple reasoning tasks such as arithmetic and parity.This raises the question of if and when Transformer models can learn the true algorithm for solving a task.We study the scope of Transformers' abilities in the spec... | What Algorithms can Transformers Learn? A Study in Length Generalization |
d14717992 | Deep reinforcement learning agents have achieved state-of-the-art results by directly maximising cumulative reward. However, environments contain a much wider variety of possible training signals. In this paper, we introduce an agent that also maximises many other pseudo-reward functions simultaneously by reinforcement... | REINFORCEMENT LEARNING WITH UNSUPERVISED AUXILIARY TASKS |
d3515219 | In spite of the recent success of neural machine translation (NMT) in standard benchmarks, the lack of large parallel corpora poses a major practical problem for many language pairs. There have been several proposals to alleviate this issue with, for instance, triangulation and semi-supervised learning techniques, but ... | UNSUPERVISED NEURAL MACHINE TRANSLATION |
d227343966 | Inspired by human learning, researchers have proposed ordering examples during training based on their difficulty. Both curriculum learning, exposing a network to easier examples early in training, and anti-curriculum learning, showing the most difficult examples first, have been suggested as improvements to the standa... | When Do Curricula Work? |
d52877285 | Classical models describe primary visual cortex (V1) as a filter bank of orientationselective linear-nonlinear (LN) or energy models, but these models fail to predict neural responses to natural stimuli accurately. Recent work shows that models based on convolutional neural networks (CNNs) lead to much more accurate pr... | A ROTATION-EQUIVARIANT CONVOLUTIONAL NEURAL NETWORK MODEL OF PRIMARY VISUAL CORTEX |
d252992876 | With more people publishing their personal data online, unauthorized data usage has become a serious concern. The unlearnable strategies have been introduced to prevent third parties from training on the data without permission. They add perturbations to the users' data before publishing, which aims to make the models ... | TRANSFERABLE UNLEARNABLE EXAMPLES |
d238582773 | Recently, large-scale Contrastive Language-Image Pre-training (CLIP) has attracted unprecedented attention for its impressive zero-shot recognition ability and excellent transferability to downstream tasks. However, CLIP is quite data-hungry and requires 400M image-text pairs for pre-training, thereby restricting its ... | SUPERVISION EXISTS EVERYWHERE: A DATA EFFICIENT CONTRASTIVE LANGUAGE-IMAGE PRE-TRAINING PARADIGM |
d257985547 | Motion mimicking is a foundational task in physics-based character animation. However, most existing motion mimicking methods are built upon reinforcement learning (RL) and suffer from heavy reward engineering, high variance, and slow convergence with hard explorations. Specifically, they usually take tens of hours or ... | DIFFMIMIC: EFFICIENT MOTION MIMICKING WITH DIFFERENTIABLE PHYSICS |
d247570285 | As machine learning models are deployed ever more broadly, it becomes increasingly important that they are not only able to perform well on their training distribution, but also yield accurate predictions when confronted with distribution shift. The Distributionally Robust Optimization (DRO) framework proposes to addre... | DISTRIBUTIONALLY ROBUST MODELS WITH PARAMETRIC LIKELIHOOD RATIOS |
d264306248 | Previous motion generation methods are limited to the pre-rigged 3D human model, hindering their applications in the animation of various non-rigged characters.In this work, we present TapMo, a Text-driven Animation Pipeline for synthesizing Motion in a broad spectrum of skeleton-free 3D characters.The pivotal innovati... | TAPMO: SHAPE-AWARE MOTION GENERATION OF SKELETON-FREE CHARACTERS |
d258332176 | Computational simulation of chemical and biological systems using ab initio molecular dynamics has been a challenge over decades. Researchers have attempted to address the problem with machine learning and fragmentation-based methods. However, the two approaches fail to give a satisfactory description of long-range and... | Long-Short-Range Message-Passing: A Physics-Informed Framework to Capture Non-Local Interaction for Scalable Molecular Dynamics Simulation |
d84591 | The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing generators learn to "linearize semantics" in the latent space of such model... | Adversarial Feature Learning |
d226278023 | We present neural architectures that disentangle RGB-D images into objects' shapes and styles and a map of the background scene, and explore their applications for few-shot 3D object detection and few-shot concept classification. Our networks incorporate architectural biases that reflect the image formation process, 3D... | DISENTANGLING 3D PROTOTYPICAL NETWORKS FOR FEW-SHOT CONCEPT LEARNING |
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