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d238419231 | A well-known line of work[Barron, 1993, Breiman, 1993, Klusowski and Barron, 2018provides bounds on the width n of a ReLU two-layer neural network needed to approximate a function f over the ball BR(R d ) up to error , when the Fourier based quantity[2019] used the Radon transform as a tool for analysis of infinite-wid... | Tighter Sparse Approximation Bounds for ReLU Neural Networks |
d256275098 | This paper proposes a simple method to distill and detect backdoor patterns within an image: Cognitive Distillation (CD). The idea is to extract the "minimal essence" from an input image responsible for the model's prediction. CD optimizes an input mask to extract a small pattern from the input image that can lead to t... | DISTILLING COGNITIVE BACKDOOR PATTERNS WITHIN AN IMAGE |
d219604274 | After training on large datasets, certain deep neural networks are surprisingly good models of the neural mechanisms of adult primate visual object recognition. Nevertheless, these models are poor models of the development of the visual system because they posit millions of sequential, precisely coordinated synaptic up... | Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream |
d258865597 | Recent research has highlighted the vulnerability of Deep Neural Networks (DNNs) against data poisoning attacks. These attacks aim to inject poisoning samples into the models' training dataset such that the trained models have inference failures. While previous studies have executed different types of attacks, one majo... | Sharpness-Aware Data Poisoning Attack |
d210116632 | Generative Adversarial Imitation Learning (GAIL) is a powerful and practical approach for learning sequential decision-making policies. Different from Reinforcement Learning (RL), GAIL takes advantage of demonstration data by experts (e.g., human), and learns both the policy and reward function of the unknown environme... | On Computation and Generalization of Generative Adversarial Imitation Learning |
d245123905 | In this paper we propose a new generative model of text, Step-unrolled Denoising Autoencoder (SUNDAE), that does not rely on autoregressive models. Similarly to denoising diffusion techniques, SUNDAE is repeatedly applied on a sequence of tokens, starting from random inputs and improving them each time until convergenc... | STEP-UNROLLED DENOISING AUTOENCODERS FOR TEXT GENERATION |
d264490642 | With LLMs shifting their role from statistical modeling of language to serving as general-purpose AI agents, how should LLM evaluations change?Arguably, a key ability of an AI agent is to flexibly combine, as needed, the basic skills it has learned.The capability to combine skills plays an important role in (human) ped... | SKILL-MIX: A FLEXIBLE AND EXPANDABLE FAMILY OF EVALUATIONS FOR AI MODELS |
d236777112 | Realistic synthetic time series data of sufficient length enables practical applications in time series modeling tasks, such as forecasting, but remains a challenge. In this paper, we present PSA-GAN, a generative adversarial network (GAN) that generates long time series samples of high quality using progressive growin... | PSA-GAN: PROGRESSIVE SELF ATTENTION GANS FOR SYNTHETIC TIME SERIES |
d56475888 | Humans and animals can learn complex predictive models that allow them to accurately and reliably reason about real-world phenomena, and they can adapt such models extremely quickly in the face of unexpected changes. Deep neural network models allow us to represent very complex functions, but lack this capacity for rap... | DEEP ONLINE LEARNING VIA META-LEARNING: CONTINUAL ADAPTATION FOR MODEL-BASED RL |
d263605760 | Offline reinforcement learning (RL), where the agent aims to learn the optimal policy based on the data collected by a behavior policy, has attracted increasing attention in recent years.While offline RL with linear function approximation has been extensively studied with optimal results achieved under certain assumpti... | Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning |
d263622244 | Generating realistic time series data is important for many engineering and scientific applications.Existing work tackles this problem using generative adversarial networks (GANs).However, GANs are often unstable during training, and they can suffer from mode collpase.While variational autoencoders (VAEs) are known to ... | GENERATIVE MODELING OF REGULAR AND IRREGULAR TIME SERIES DATA VIA KOOPMAN VAES |
d251320459 | We provide a convergence analysis of gradient descent for the problem of agnostically learning a single ReLU function under Gaussian distributions. Unlike prior work that studies the setting of zero bias, we consider the more challenging scenario when the bias of the ReLU function is non-zero. Our main result establish... | Agnostic Learning of General ReLU Activation Using Gradient Descent |
d53047456 | Several recent works have developed methods for training classifiers that are certifiably robust against norm-bounded adversarial perturbations. However, these methods assume that all the adversarial transformations provide equal value for adversaries, which is seldom the case in real-world applications. We advocate fo... | Cost-Sensitive Robustness against Adversarial Examples |
d254823387 | Post-hoc explanation methods are used with the intent of providing insights about neural networks and are sometimes said to help engender trust in their outputs. However, popular explanations methods have been found to be fragile to minor perturbations of input features or model parameters. Relying on constraint relaxa... | ROBUST EXPLANATION CONSTRAINTS FOR NEURAL NETWORKS |
d238198466 | Both animals and artificial agents benefit from state representations that support rapid transfer of learning across tasks and which enable them to efficiently traverse their environments to reach rewarding states. The successor representation (SR), which measures the expected cumulative, discounted state occupancy und... | A FIRST-OCCUPANCY REPRESENTATION FOR REINFORCEMENT LEARNING |
d256615681 | Using the notion of conservative gradient, we provide a simple model to estimate the computational costs of the backward and forward modes of algorithmic differentiation for a wide class of nonsmooth programs. The overhead complexity of the backward mode turns out to be independent of the dimension when using programs ... | ON THE COMPLEXITY OF NONSMOOTH AUTOMATIC DIFFERENTIATION |
d263605617 | Conventional wisdom suggests that neural network predictions tend to be unpredictable and overconfident when faced with out-of-distribution (OOD) inputs.Our work reassesses this assumption for neural networks with high-dimensional inputs.Rather than extrapolating in arbitrary ways, we observe that neural network predic... | Deep Neural Networks Tend To Extrapolate Predictably |
d264555382 | Recent years have seen growing interest in developing and applying perceptual similarity metrics.Research has shown the superiority of perceptual metrics over pixel-wise metrics in aligning with human perception and serving as a proxy for the human visual system.On the other hand, as perceptual metrics rely on neural n... | LIPSIM: A PROVABLY ROBUST PERCEPTUAL SIMILARITY METRIC |
d253107613 | We propose Algorithm Distillation (AD), a method for distilling reinforcement learning (RL) algorithms into neural networks by modeling their training histories with a causal sequence model. Algorithm Distillation treats learning to reinforcement learn as an across-episode sequential prediction problem. A dataset of le... | IN-CONTEXT REINFORCEMENT LEARNING WITH ALGORITHM DISTILLATION |
d259298561 | We study the type of solutions to which stochastic gradient descent converges when used to train a single hidden-layer multivariate ReLU network with the quadratic loss. Our results are based on a dynamical stability analysis. In the univariate case, it was shown that linearly stable minima correspond to network functi... | THE IMPLICIT BIAS OF MINIMA STABILITY IN MULTIVARIATE SHALLOW RELU NETWORKS |
d238583704 | Solutions to the Traveling Salesperson Problem (TSP) have practical applications to processes in transportation, logistics, and automation, yet must be computed with minimal delay to satisfy the real-time nature of the underlying tasks. However, solving large TSP instances quickly without sacrificing solution quality r... | GRAPH NEURAL NETWORK GUIDED LOCAL SEARCH FOR THE TRAVELING SALESPERSON PROBLEM |
d252683968 | This work analyzes the solution trajectory of gradient-based algorithms via a novel basis function decomposition. We show that, although solution trajectories of gradient-based algorithms may vary depending on the learning task, they behave almost monotonically when projected onto an appropriate orthonormal function ba... | Behind the Scenes of Gradient Descent: A Trajectory Analysis via Basis Function Decomposition |
d52948121 | Deep Learning for Computer Vision depends mainly on the source of supervision. Photo-realistic simulators can generate large-scale automatically labeled synthetic data, but introduce a domain gap negatively impacting performance. We propose a new unsupervised domain adaptation algorithm, called SPIGAN, relying on Simul... | SPIGAN: PRIVILEGED ADVERSARIAL LEARNING FROM SIMULATION |
d238408308 | Stateful optimizers maintain gradient statistics over time, e.g., the exponentially smoothed sum (SGD with momentum) or squared sum (Adam) of past gradient values. This state can be used to accelerate optimization compared to plain stochastic gradient descent but uses memory that might otherwise be allocated to model p... | 8-BIT OPTIMIZERS VIA BLOCK-WISE QUANTIZATION |
d44119895 | Deep learning systems have become ubiquitous in many aspects of our lives. Unfortunately, it has been shown that such systems are vulnerable to adversarial attacks, making them prone to potential unlawful and harmful uses. Designing deep neural networks that are robust to adversarial attacks is a fundamental step in ma... | PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks |
d186206768 | It has been established that diverse behaviors spanning the controllable subspace of a Markov decision process can be trained by rewarding a policy for being distinguishable from other policies(Gregor et al., 2016;Eysenbach et al., 2018;Warde-Farley et al., 2018). However, one limitation of this formulation is the diff... | Fast Task Inference with Variational Intrinsic Successor Features |
d49659296 | A. Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond. By necessity, most theoretical guarantees revolve around convex-concave (or even linear) problems; however, making theoretical inroads towards e c... | OPTIMISTIC MIRROR DESCENT IN SADDLE-POINT PROBLEMS: GOING THE EXTRA (GRADIENT) MILE |
d221376626 | Dense embedding models are commonly deployed in commercial search engines, wherein all the document vectors are pre-computed, and near-neighbor search (NNS) is performed with the query vector to find relevant documents. However, the bottleneck of indexing a large number of dense vectors and performing an NNS hurts the ... | SOLAR: Sparse Orthogonal Learned and Random Embeddings |
d24510394 | Learning to align distributions by minimizing an adversarial distance between them has recently achieved impressive results. However, such models are difficult to optimize with gradient descent and they often do not converge without very careful parameter tuning and initialization. We investigate whether turning the ad... | Stable Distribution Alignment Using the Dual of the Adversarial Distance |
d247628166 | We study how the choice of visual perspective affects learning and generalization in the context of physical manipulation from raw sensor observations. Compared with the more commonly used global third-person perspective, a hand-centric (eye-in-hand) perspective affords reduced observability, but we find that it consis... | VISION-BASED MANIPULATORS NEED TO ALSO SEE FROM THEIR HANDS |
d256615676 | This paper presents Universal Vision-Language Dense Retrieval (UniVL-DR), which builds a unified model for multi-modal retrieval. UniVL-DR encodes queries and multi-modality resources in an embedding space for searching candidates from different modalities. To learn a unified embedding space for multi-modal retrieval, ... | UNIVERSAL VISION-LANGUAGE DENSE RETRIEVAL: LEARNING A UNIFIED REPRESENTATION SPACE FOR MULTI-MODAL RETRIEVAL |
d44065826 | We present Value Propagation (VProp), a set of parameter-efficient differentiable planning modules built on Value Iteration which can successfully be trained using reinforcement learning to solve unseen tasks, has the capability to generalize to larger map sizes, and can learn to navigate in dynamic environments. We sh... | Value Propagation Networks |
d52892910 | Many complex domains, such as robotics control and real-time strategy (RTS) games, require an agent to learn a continuous control. In the former, an agent learns a policy over R d and in the latter, over a discrete set of actions each of which is parametrized by a continuous parameter. Such problems are naturally solve... | Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications |
d211259464 | Deep reinforcement learning is successful in decision making for sophisticated games, such as Atari, Go, etc.However, real-world decision making often requires reasoning with partial information extracted from complex visual observations.This paper presents Discriminative Particle Filter Reinforcement Learning (DPFRL),... | DISCRIMINATIVE PARTICLE FILTER REINFORCEMENT LEARNING FOR COMPLEX PARTIAL OBSERVATIONS |
d264289064 | Many applications of large language models (LLMs), ranging from chatbots to creative writing, require nuanced subjective judgments that can differ significantly across different groups.Existing alignment algorithms can be expensive to align for each group, requiring prohibitive amounts of group-specific preference data... | GROUP PREFERENCE OPTIMIZATION: FEW-SHOT ALIGNMENT OF LARGE LANGUAGE MODELS |
d3067546 | We introduce a design strategy for neural network macro-architecture based on selfsimilarity. Repeated application of a single expansion rule generates an extremely deep network whose structural layout is precisely a truncated fractal. Such a network contains interacting subpaths of different lengths, but does not incl... | FractalNet: Ultra-Deep Neural Networks without Residuals |
d248887644 | We introduce an optimal transport-based model for learning a metric tensor from cross-sectional samples of evolving probability measures on a common Riemannian manifold. We neurally parametrize the metric as a spatially-varying matrix field and efficiently optimize our model's objective using a simple alternating schem... | RIEMANNIAN METRIC LEARNING VIA OPTIMAL TRANSPORT |
d16889645 | The high-dimensional and redundant nature of video have pushed researchers to seek the design of attentional models that can dynamically focus computations on the spatiotemporal volumes that are most relevant. Specifically, these models have been used to eliminate or down-weight background pixels that are not important... | Recurrent Mixture Density Network for Spatiotemporal Visual Attention |
d218522109 | The vulnerabilities of deep neural networks against adversarial examples have become a significant concern for deploying these models in sensitive domains. Devising a definitive defense against such attacks is proven to be challenging, and the methods relying on detecting adversarial samples are only valid when the att... | GAT: GENERATIVE ADVERSARIAL TRAINING FOR ADVERSARIAL EXAMPLE DETECTION AND ROBUST CLASSIFICATION |
d219965819 | Differential equations are a natural choice for modeling recurrent neural networks because they can be viewed as dynamical systems with a driving input. In this work, we propose a recurrent unit that describes the hidden state's evolution with two parts: a well-understood linear component plus a Lipschitz nonlinearity.... | LIPSCHITZ RECURRENT NEURAL NETWORKS |
d247518637 | Interval Bound Propagation (IBP) is so far the base of state-of-the-art methods for training neural networks with certifiable robustness guarantees when potential adversarial perturbations present, while the convergence of IBP training remains unknown in existing literature. In this paper, we present a theoretical anal... | ON THE CONVERGENCE OF CERTIFIED ROBUST TRAINING WITH INTERVAL BOUND PROPAGATION |
d220830766 | Program synthesis is challenging largely because of the difficulty of search in a large space of programs.Human programmers routinely tackle the task of writing complex programs by writing sub-programs and then analyzing their intermediate results to compose them in appropriate ways.Motivated by this intuition, we pres... | BUSTLE: BOTTOM-UP PROGRAM SYNTHESIS THROUGH LEARNING-GUIDED EXPLORATION |
d259164815 | Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated exceptional performance in code-related tasks. However, most existing models are solely pre-trained on extensive raw code data without instruction finetuning. In this paper, we introduce WizardCoder, which empowers Code LLMs with complex instr... | WizardCoder: Empowering Code Large Language Models with Evol-Instruct |
d264172260 | This work studies training instabilities of behavior cloning with deep neural networks. We observe that minibatch SGD updates to the policy network during training result in sharp oscillations in longhorizon rewards, despite negligibly affecting the behavior cloning loss. We empirically disentangle the statistical and ... | Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression |
d244773166 | The topological patterns exhibited by many real-world networks motivate the development of topology-based methods for assessing the similarity of networks. However, extracting topological structure is difficult, especially for large and dense networks whose node degrees range over multiple orders of magnitude. In this ... | FAST TOPOLOGICAL CLUSTERING WITH WASSERSTEIN DISTANCE |
d253523197 | In this paper we look into the conjecture ofEntezari et al. (2021)which states that if the permutation invariance of neural networks is taken into account, then there is likely no loss barrier to the linear interpolation between SGD solutions.First, we observe that neuron alignment methods alone are insufficient to est... | REPAIR: REnormalizing Permuted Activations for Interpolation Repair |
d259164684 | In Online Continual Learning (OCL) a learning system receives a stream of data and sequentially performs prediction and training steps. Important challenges in OCL are concerned with automatic adaptation to the particular non-stationary structure of the data, and with quantification of predictive uncertainty. Motivated... | Kalman Filter for Online Classification of Non-Stationary Data |
d204838340 | Often we wish to transfer representational knowledge from one neural network to another.Examples include distilling a large network into a smaller one, transferring knowledge from one sensory modality to a second, or ensembling a collection of models into a single estimator.Knowledge distillation, the standard approach... | CONTRASTIVE REPRESENTATION DISTILLATION |
d249191541 | Safe reinforcement learning (RL) trains a policy to maximize the task reward while satisfying safety constraints. While prior works focus on the performance optimality, we find that the optimal solutions of many safe RL problems are not robust and safe against carefully designed observational perturbations. We formally... | ON THE ROBUSTNESS OF SAFE REINFORCEMENT LEARNING UNDER OBSERVATIONAL PERTURBATIONS |
d822804 | Many NLP tasks including machine comprehension, answer selection and text entailment require the comparison between sequences. Matching the important units between sequences is a key to solve these problems. In this paper, we present a general "compare-aggregate" framework that performs word-level matching followed by ... | A COMPARE-AGGREGATE MODEL FOR MATCHING TEXT SEQUENCES |
d252595808 | Open-domain dialogue systems aim to interact with humans through natural language texts in an open-ended fashion. Despite the recent success of super large dialogue systems such as ChatGPT, using medium-to-small-sized dialogue systems remains the common practice as they are more lightweight and accessible; however, gen... | AN EQUAL-SIZE HARD EM ALGORITHM FOR DIVERSE DIALOGUE GENERATION |
d17786716 | An emerging design principle in deep learning is that each layer of a deep artificial neural network should be able to easily express the identity transformation. This idea not only motivated various normalization techniques, such as batch normalization, but was also key to the immense success of residual networks.In t... | Identity Matters in Deep Learning |
d108299957 | Recent literature suggests that averaged word vectors followed by simple postprocessing outperform many deep learning methods on semantic textual similarity tasks. Furthermore, when averaged word vectors are trained supervised on large corpora of paraphrases, they achieve state-of-the-art results on standard STS benchm... | DON'T SETTLE FOR AVERAGE, GO FOR THE MAX: FUZZY SETS AND MAX-POOLED WORD VECTORS |
d231855326 | The gradient descent-ascent (GDA) algorithm has been widely applied to solve minimax optimization problems. In order to achieve convergent policy parameters for minimax optimization, it is important that GDA generates convergent variable sequences rather than convergent sequences of function values or gradient norms. H... | PROXIMAL GRADIENT DESCENT-ASCENT: VARIABLE CONVERGENCE UNDER KŁ GEOMETRY |
d255393757 | Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and v... | POLICY PRE-TRAINING FOR AUTONOMOUS DRIVING VIA SELF-SUPERVISED GEOMETRIC MODELING |
d31816657 | Understanding procedural language requires anticipating the causal effects of actions, even when they are not explicitly stated. In this work, we introduce Neural Process Networks to understand procedural text through (neural) simulation of action dynamics. Our model complements existing memory architectures with dynam... | SIMULATING ACTION DYNAMICS WITH NEURAL PROCESS NETWORKS |
d189898655 | Detecting objects such as cars and pedestrians in 3D plays an indispensable role in autonomous driving.Existing approaches largely rely on expensive LiDAR sensors for accurate depth information.While recently pseudo-LiDAR has been introduced as a promising alternative, at a much lower cost based solely on stereo images... | PSEUDO-LIDAR++: ACCURATE DEPTH FOR 3D OBJECT DETECTION IN AUTONOMOUS DRIVING |
d59317031 | Normalization layers are a staple in state-of-the-art deep neural network architectures. They are widely believed to stabilize training, enable higher learning rate, accelerate convergence and improve generalization, though the reason for their effectiveness is still an active research topic. In this work, we challenge... | FIXUP INITIALIZATION: RESIDUAL LEARNING WITHOUT NORMALIZATION |
d244478211 | The lottery ticket hypothesis conjectures the existence of sparse subnetworks of large randomly initialized deep neural networks that can be successfully trained in isolation.Recent work has experimentally observed that some of these tickets can be practically reused across a variety of tasks, hinting at some form of u... | ON THE EXISTENCE OF UNIVERSAL LOTTERY TICKETS |
d264128429 | In this work we present a new method for the estimation of MUTUAL INFOR-MATION (MI) between random variables.Our approach is based on an original interpretation of the Girsanov theorem, which allows us to use score-based diffusion models to estimate the KULLBACK-LEIBLER (KL) divergence between two densities as a differ... | MINDE: MUTUAL INFORMATION NEURAL DIFFUSION ESTIMATION |
d4994434 | An efficient learner is one who reuses what they already know to tackle a new problem. For a machine learner, this means understanding the similarities amongst datasets. In order to do this, one must take seriously the idea of working with datasets, rather than datapoints, as the key objects to model. Towards this goal... | Towards a Neural Statistician |
d219531578 | Imitation Learning (IL) methods seek to match the behavior of an agent with that of an expert. In the present work, we propose a new IL method based on a conceptually simple algorithm: Primal Wasserstein Imitation Learning (PWIL), which ties to the primal form of the Wasserstein distance between the expert and the agen... | Primal Wasserstein Imitation Learning |
d246485695 | We explore the use of expert iteration in the context of language modeling applied to formal mathematics. We show that at same compute budget, expert iteration, by which we mean proof search interleaved with learning, dramatically outperforms proof search only. We also observe that when applied to a collection of forma... | Formal Mathematics Statement Curriculum Learning |
d5776935 | Partial differential equations (PDEs) play a prominent role in many disciplines such as applied mathematics, physics, chemistry, material science, computer science, etc. PDEs are commonly derived based on physical laws or empirical observations. However, the governing equations for many complex systems in modern applic... | PDE-NET: LEARNING PDES FROM DATA |
d258987919 | Randomized smoothing-based certification is an effective approach for obtaining robustness certificates of deep neural networks (DNNs) against adversarial attacks. This method constructs a smoothed DNN model and certifies its robustness through statistical sampling, but it is computationally expensive, especially when ... | Incremental Randomized Smoothing Certification |
d247867757 | Populations have often been perceived as a structuring component for language to emerge and evolve: the larger the population, the more structured the language. While this observation is widespread in the sociolinguistic literature, it has not been consistently reproduced in computer simulations with neural agents. In ... | ON THE ROLE OF POPULATION HETEROGENEITY IN EMERGENT COMMUNICATION |
d249209990 | Concept Bottleneck Models (CBMs) map the inputs onto a set of interpretable concepts ("the bottleneck") and use the concepts to make predictions. A concept bottleneck enhances interpretability since it can be investigated to understand what concepts the model "sees" in an input and which of these concepts are deemed im... | POST-HOC CONCEPT BOTTLENECK MODELS |
d53443065 | In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein (W.) barycenters. Optimal transport metrics, such as the Wasserstein distance, allow incorporating semantic side information such as word embeddings. Using W. barycenters to find the consensus between... | WASSERSTEIN BARYCENTER MODEL ENSEMBLING |
d254854448 | Graph neural networks (GNNs), as the de-facto model class for representation learning on graphs, are built upon the multi-layer perceptrons (MLP) architecture with additional message passing layers to allow features to flow across nodes. While conventional wisdom commonly attributes the success of GNNs to their advance... | GRAPH NEURAL NETWORKS ARE INHERENTLY GOOD GENERALIZERS: INSIGHTS BY BRIDGING GNNS AND MLPS |
d224706002 | Differentiable rendering has paved the way to training neural networks to perform "inverse graphics" tasks such as predicting 3D geometry from monocular photographs. To train high performing models, most of the current approaches rely on multi-view imagery which are not readily available in practice. Recent Generative ... | IMAGE GANS MEET DIFFERENTIABLE RENDERING FOR INVERSE GRAPHICS AND INTERPRETABLE 3D NEURAL RENDERING |
d257365911 | Out-of-distribution (OOD) detection is indispensable for safely deploying machine learning models in the wild. One of the key challenges is that models lack supervision signals from unknown data, and as a result, can produce overconfident predictions on OOD data. Recent work on outlier synthesis modeled the feature spa... | NON-PARAMETRIC OUTLIER SYNTHESIS |
d59553561 | Analogical reasoning has been a principal focus of various waves of AI research. Analogy is particularly challenging for machines because it requires relational structures to be represented such that they can be flexibly applied across diverse domains of experience. Here, we study how analogical reasoning can be induce... | LEARNING TO MAKE ANALOGIES BY CONTRASTING ABSTRACT RELATIONAL STRUCTURE |
d211146563 | Sliced-Wasserstein distance (SWD) and its variation, Max Sliced-Wasserstein distance (Max-SWD), have been widely used in the recent years due to their fast computation and scalability when the probability measures lie in very high dimension. However, these distances still have their weaknesses. In particular, SWD requi... | Distributional Sliced-Wasserstein and Applications to Generative Modeling |
d6771196 | There is a lot of research interest in encoding variable length sentences into fixed length vectors, in a way that preserves the sentence meanings. Two common methods include representations based on averaging word vectors, and representations based on the hidden states of recurrent neural networks such as LSTMs. The s... | FINE-GRAINED ANALYSIS OF SENTENCE EMBEDDINGS USING AUXILIARY PREDICTION TASKS |
d264490587 | Treatment effect estimation in continuous time is crucial for personalized medicine.However, existing methods for this task are limited to point estimates of the potential outcomes, whereas uncertainty estimates have been ignored.Needless to say, uncertainty quantification is crucial for reliable decision-making in med... | BAYESIAN NEURAL CONTROLLED DIFFERENTIAL EQUATIONS FOR TREATMENT EFFECT ESTIMATION |
d256390192 | Recently, the Successor Features and Generalized Policy Improvement (SF&GPI) framework has been proposed as a method for learning, composing, and transferring predictive knowledge and behavior. SF&GPI works by having an agent learn predictive representations (SFs) that can be combined for transfer to new tasks with GPI... | Composing Task Knowledge with Modular Successor Feature Approximators |
d247519239 | In many prediction problems, spurious correlations are induced by a changing relationship between the label and a nuisance variable that is also correlated with the covariates. For example, in classifying animals in natural images, the background, which is a nuisance, can predict the type of animal. This nuisance-label... | Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations |
d76667896 | Humans easily recognize object parts and their hierarchical structure by watching how they move; they can then predict how each part moves in the future. In this paper, we propose a novel formulation that simultaneously learns a hierarchical, disentangled object representation and a dynamics model for object parts from... | UNSUPERVISED DISCOVERY OF PARTS, STRUCTURE, AND DYNAMICS |
d249926641 | We present a novel graph neural network we call AgentNet, which is designed specifically for graph-level tasks. AgentNet is inspired by sublinear algorithms, featuring a computational complexity that is independent of the graph size. The architecture of AgentNet differs fundamentally from the architectures of tradition... | AGENT-BASED GRAPH NEURAL NETWORKS |
d53388625 | Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy. However, contemporary experience is that the sparse architectures produced by pruning are difficult to tr... | THE LOTTERY TICKET HYPOTHESIS: FINDING SPARSE, TRAINABLE NEURAL NETWORKS |
d246824069 | Robust multi-agent trajectory prediction is essential for the safe control of robotic systems. A major challenge is to efficiently learn a representation that approximates the true joint distribution of contextual, social, and temporal information to enable planning. We propose Latent Variable Sequential Set Transforme... | LATENT VARIABLE SEQUENTIAL SET TRANSFORMERS FOR JOINT MULTI-AGENT MOTION PREDICTION |
d13764176 | A popular recent approach to answering open-domain questions is to first search for question-related passages and then apply reading comprehension models to extract answers. Existing methods usually extract answers from single passages independently. But some questions require a combination of evidence from across diff... | EVIDENCE AGGREGATION FOR ANSWER RE-RANKING IN OPEN-DOMAIN QUESTION ANSWERING |
d259861505 | The use of pretrained deep neural networks represents an attractive way to achieve strong results with few data available.When specialized in dense problems such as object detection, learning local rather than global information in images has proven to be more efficient.However, for unsupervised pretraining, the popula... | PROPOSAL-CONTRASTIVE PRETRAINING FOR OBJECT DETECTION FROM FEWER DATA |
d239016408 | Committee-based models (ensembles or cascades) construct models by combining existing pre-trained ones. While ensembles and cascades are well-known techniques that were proposed before deep learning, they are not considered a core building block of deep model architectures and are rarely compared to in recent literatur... | WISDOM OF COMMITTEES: AN OVERLOOKED APPROACH TO FASTER AND MORE ACCURATE MODELS |
d53109277 | We introduce the problem of learning distributed representations of edits. By combining a "neural editor" with an "edit encoder", our models learn to represent the salient information of an edit and can be used to apply edits to new inputs. We experiment on natural language and source code edit data. Our evaluation yie... | LEARNING TO REPRESENT EDITS |
d238583726 | Pretraining Neural Language Models (NLMs) over a large corpus involves chunking the text into training examples, which are contiguous text segments of sizes processable by the neural architecture. We highlight a bias introduced by this common practice: we prove that the pretrained NLM can model much stronger dependenci... | THE INDUCTIVE BIAS OF IN-CONTEXT LEARNING: RETHINKING PRETRAINING EXAMPLE DESIGN |
d231942749 | We present lambda layers -an alternative framework to self-attention -for capturing long-range interactions between an input and structured contextual information (e.g. a pixel surrounded by other pixels). Lambda layers capture such interactions by transforming available contexts into linear functions, termed lambdas, ... | LAMBDANETWORKS: MODELING LONG-RANGE INTERACTIONS WITHOUT ATTENTION |
d67855441 | We investigate the internal representations that a recurrent neural network (RNN) uses while learning to recognize a regular formal language. Specifically, we train a RNN on positive and negative examples from a regular language, and ask if there is a simple decoding function that maps states of this RNN to states of t... | REPRESENTING FORMAL LANGUAGES: A COMPARISON BETWEEN FINITE AUTOMATA AND RECURRENT NEURAL NETWORKS |
d250113584 | Existing methods for isolating hard subpopulations and spurious correlations in datasets often require human intervention. This can make these methods labor-intensive and dataset-specific. To address these shortcomings, we present a scalable method for automatically distilling a model's failure modes. Specifically, we ... | Distilling Model Failures as Directions in Latent Space |
d52947902 | We address two challenges of probabilistic topic modelling in order to better estimate the probability of a word in a given context, i.e., P (word|context) : (1) No language structure in context: Probabilistic topic models ignore word order by summarizing a given context as a "bag-of-word" and consequently the semantic... | textTOvec: DEEP CONTEXTUALIZED NEURAL AUTOREGRESSIVE TOPIC MODELS OF LANGUAGE WITH DISTRIBUTED COMPOSITIONAL PRIOR |
d252873667 | While 6D object pose estimation has wide applications across computer vision and robotics, it remains far from being solved due to the lack of annotations. The problem becomes even more challenging when moving to category-level 6D pose, which requires generalization to unseen instances. Current approaches are restricte... | SELF-SUPERVISED GEOMETRIC CORRESPONDENCE FOR CATEGORY-LEVEL 6D OBJECT POSE ESTIMATION IN THE WILD |
d203837750 | Using deep neural networks that are either invariant or equivariant to permutations in order to learn functions on unordered sets has become prevalent. The most popular, basic models are DeepSets(Zaheer et al., 2017)and PointNet (Qi et al., 2017). While known to be universal for approximating invariant functions, DeepS... | ON UNIVERSAL EQUIVARIANT SET NETWORKS |
d264306288 | Large Language Models (LLMs) have excelled as high-level semantic planners for sequential decision-making tasks.However, harnessing them to learn complex lowlevel manipulation tasks, such as dexterous pen spinning, remains an open problem.We bridge this fundamental gap and present EUREKA, a human-level reward design al... | EUREKA: HUMAN-LEVEL REWARD DESIGN VIA CODING LARGE LANGUAGE MODELS |
d246473127 | Out-of-distribution (OOD) detection has received much attention lately due to its importance in the safe deployment of neural networks. One of the key challenges is that models lack supervision signals from unknown data, and as a result, can produce overconfident predictions on OOD data. Previous approaches rely on rea... | VOS: LEARNING WHAT YOU DON'T KNOW BY VIRTUAL OUTLIER SYNTHESIS |
d255998431 | Biological neural networks are capable of recruiting different sets of neurons to encode different memories. However, when training artificial neural networks on a set of tasks, typically, no mechanism is employed for selectively producing anything analogous to these neuronal ensembles. Further, artificial neural netwo... | ARTIFICIAL NEURONAL ENSEMBLES WITH LEARNED CONTEXT DEPENDENT GATING |
d247450597 | Building models of human decision-making from observed behaviour is critical to better understand, diagnose and support real-world policies such as clinical care. As established policy learning approaches remain focused on imitation performance, they fall short of explaining the demonstrated decision-making process. Po... | POETREE: INTERPRETABLE POLICY LEARNING WITH ADAPTIVE DECISION TREES |
d258967241 | Recently, diffusion models have achieved remarkable success in generating tasks, including image and audio generation.However, like other generative models, diffusion models are prone to privacy issues.In this paper, we propose an efficient query-based membership inference attack (MIA), namely Proximal Initialization A... | An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization |
d247058853 | Despite the recent success of deep learning for time series forecasting, these methods are not scalable for many real-world applications where data arrives sequentially. Training deep neural forecasters on the fly is notoriously challenging because of their limited ability to adapt to non-stationary environments and re... | LEARNING FAST AND SLOW FOR ONLINE TIME SERIES FORECASTING |
d264289134 | Federated learning has emerged as a promising distributed learning paradigm that facilitates collaborative learning among multiple parties without transferring raw data.However, most existing federated learning studies focus on either horizontal or vertical data settings, where the data of different parties are assumed... | EFFECTIVE AND EFFICIENT FEDERATED TREE LEARNING ON HYBRID DATA |
d249240324 | Ensembling certifiably robust neural networks is a promising approach for improving the certified robust accuracy of neural models. Black-box ensembles that assume only query-access to the constituent models (and their robustness certifiers) during prediction are particularly attractive due to their modular structure. ... | ON THE PERILS OF CASCADING ROBUST CLASSIFIERS |
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