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d251279905 | Computing Nash equilibrium policies is a central problem in multi-agent reinforcement learning that has received extensive attention both in theory and in practice. However, in light of computational intractability barriers in general-sum games, provable guarantees have been thus far either limited to fully competitive... | Efficiently Computing Nash Equilibria in Adversarial Team Markov Games |
d229677982 | We study adversary-resilient stochastic distributed optimization, in which m machines can independently compute stochastic gradients, and cooperate to jointly optimize over their local objective functions. However, an α-fraction of the machines are Byzantine, in that they may behave in arbitrary, adversarial ways. We c... | Byzantine-Resilient Non-Convex Stochastic Gradient Descent |
d53316620 | This paper addresses the problem of evaluating learning systems in safety critical domains such as autonomous driving, where failures can have catastrophic consequences. We focus on two problems: searching for scenarios when learned agents fail and assessing their probability of failure. The standard method for agent e... | RIGOROUS AGENT EVALUATION: AN ADVERSARIAL APPROACH TO UNCOVER CATASTROPHIC FAILURES |
d256194369 | Large Transformer-based Pretrained Language Models (PLMs) dominate almost all Natural Language Processing (NLP) tasks. Nevertheless, they still make mistakes from time to time. For a model deployed in an industrial environment, fixing these mistakes quickly and robustly is vital to improve user experiences. Previous wo... | TRANSFORMER-PATCHER: ONE MISTAKE WORTH ONE NEURON |
d238634325 | Offline reinforcement learning requires reconciling two conflicting aims: learning a policy that improves over the behavior policy that collected the dataset, while at the same time minimizing the deviation from the behavior policy so as to avoid errors due to distributional shift. This trade-off is critical, because m... | OFFLINE REINFORCEMENT LEARNING WITH IMPLICIT Q-LEARNING |
d13253311 | In classical machine learning, regression is treated as a black box process of identifying a suitable function from a hypothesis set without attempting to gain insight into the mechanism connecting inputs and outputs. In the natural sciences, however, finding an interpretable function for a phenomenon is the prime goal... | Extrapolation and learning equations |
d209960830 | We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning. We focus on a backtracking search algorithm, which can already solve formulas of impressive size -up to hundreds of thousands of variables. The main challenge is to fi... | LEARNING HEURISTICS FOR QUANTIFIED BOOLEAN FORMULAS THROUGH REINFORCEMENT LEARNING |
d170079235 | Learning to represent videos is a very challenging task both algorithmically and computationally. Standard video CNN architectures have been designed by directly extending architectures devised for image understanding to a third dimension (using a limited number of space-time modules such as 3D convolutions) or by intr... | AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures |
d48365044 | Recurrent neural networks (RNNs) are powerful architectures to model sequential data, due to their capability to learn short and long-term dependencies between the basic elements of a sequence. Nonetheless, popular tasks such as speech or images recognition, involve multi-dimensional input features that are characteriz... | QUATERNION RECURRENT NEURAL NETWORKS |
d235377428 | Generative models are now capable of producing highly realistic images that look nearly indistinguishable from the data on which they are trained.This raises the question: if we have good enough generative models, do we still need datasets?We investigate this question in the setting of learning general-purpose visual r... | GENERATIVE MODELS AS A DATA SOURCE FOR MULTIVIEW REPRESENTATION LEARNING |
d244129839 | Recent work has shown that models trained to the same objective, and which achieve similar measures of accuracy on consistent test data, may nonetheless behave very differently on individual predictions. This inconsistency is undesirable in high-stakes contexts, such as medical diagnosis and finance. We show that this ... | SELECTIVE ENSEMBLES FOR CONSISTENT PREDICTIONS |
d253581623 | Extensive work has demonstrated that equivariant neural networks can significantly improve sample efficiency and generalization by enforcing an inductive bias in the network architecture. These applications typically assume that the domain symmetry is fully described by explicit transformations of the model inputs and ... | THE SURPRISING EFFECTIVENESS OF EQUIVARIANT MODELS IN DOMAINS WITH LATENT SYMMETRY |
d211126562 | Model-based reinforcement learning has been empirically demonstrated as a successful strategy to improve sample efficiency. In particular, Dyna is an elegant model-based architecture integrating learning and planning that provides huge flexibility of using a model. One of the most important components in Dyna is called... | FREQUENCY-BASED SEARCH-CONTROL IN DYNA |
d235358966 | Meta-learning enables algorithms to quickly learn a newly encountered task with just a few labeled examples by transferring previously learned knowledge. However, the bottleneck of current meta-learning algorithms is the requirement of a large number of meta-training tasks, which may not be accessible in real-world sce... | META-LEARNING WITH FEWER TASKS THROUGH TASK INTERPOLATION |
d238743895 | We study non-convex subgradient flows for training two-layer ReLU neural networks from a convex geometry and duality perspective. We characterize the implicit bias of unregularized non-convex gradient flow as convex regularization of an equivalent convex model. We then show that the limit points of non-convex subgradie... | The Convex Geometry of Backpropagation: Neural Network Gradient Flows Converge to Extreme Points of the Dual Convex Program |
d259841016 | Semi-implicit variational inference (SIVI) greatly enriches the expressiveness of variational families by considering implicit variational distributions defined in a hierarchical manner. However, due to the intractable densities of variational distributions, current SIVI approaches often use surrogate evidence lower bo... | SEMI-IMPLICIT VARIATIONAL INFERENCE VIA SCORE MATCHING |
d261031179 | Much of the knowledge encoded in transformer language models (LMs) may be expressed in terms of relations: relations between words and their synonyms, entities and their attributes, etc. We show that, for a subset of relations, this computation is well-approximated by a single linear transformation on the subject repre... | Linearity of Relation Decoding in Transformer Language Models |
d247778706 | Efficient performance estimation of architectures drawn from large search spaces is essential to Neural Architecture Search. One-Shot methods tackle this challenge by training one supernet to approximate the performance of every architecture in the search space via weight-sharing, thereby drastically reducing the searc... | GENERALIZING FEW-SHOT NAS WITH GRADIENT MATCHING |
d225075792 | We identify an implicit under-parameterization phenomenon in value-based deep RL methods that use bootstrapping: when value functions, approximated using deep neural networks, are trained with gradient descent using iterated regression onto target values generated by previous instances of the value network, more gradie... | IMPLICIT UNDER-PARAMETERIZATION INHIBITS DATA-EFFICIENT DEEP REINFORCEMENT LEARNING |
d259088961 | Dynamic feature selection, where we sequentially query features to make accurate predictions with a minimal budget, is a promising paradigm to reduce feature acquisition costs and provide transparency into a model's predictions.The problem is challenging, however, as it requires both predicting with arbitrary feature s... | ESTIMATING CONDITIONAL MUTUAL INFORMATION FOR DYNAMIC FEATURE SELECTION |
d220404588 | Differentially private SGD (DP-SGD) is one of the most popular methods for solving differentially private empirical risk minimization (ERM). Due to its noisy perturbation on each gradient update, the error rate of DP-SGD scales with the ambient dimension p, the number of parameters in the model. Such dependence can be ... | Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification |
d229363613 | Existing literature in Continual Learning (CL) has focused on overcoming catastrophic forgetting, the inability of the learner to recall how to perform tasks observed in the past. There are however other desirable properties of a CL system, such as the ability to transfer knowledge from previous tasks and to scale memo... | EFFICIENT CONTINUAL LEARNING WITH MODULAR NETWORKS AND TASK-DRIVEN PRIORS |
d257353362 | Estimating the 3DoF rotation from a single RGB image is an important yet challenging problem. Probabilistic rotation regression has raised more and more attention with the benefit of expressing uncertainty information along with the prediction. Though modeling noise using Gaussian-resembling Bingham distribution and ma... | A LAPLACE-INSPIRED DISTRIBUTION ON SO(3) FOR PROBABILISTIC ROTATION ESTIMATION |
d264490556 | Positioned between pre-training and user deployment, aligning large language models (LLMs) through reinforcement learning (RL) has emerged as a prevailing strategy for training instruction following-models such as ChatGPT.In this work, we initiate the study of privacy-preserving alignment of LLMs through Differential P... | Privately Aligning Language Models with Reinforcement Learning |
d257687205 | Machine Learning (ML) models have been utilized for malware detection for over two decades.Consequently, this ignited an ongoing arms race between malware authors and antivirus systems, compelling researchers to propose defenses for malware-detection models against evasion attacks.However, most if not all existing defe... | DRSM: DE-RANDOMIZED SMOOTHING ON MALWARE CLASSIFIER PROVIDING CERTIFIED ROBUSTNESS |
d220363897 | Many successful deep learning architectures are equivariant to certain transformations in order to conserve parameters and improve generalization: most famously, convolution layers are equivariant to shifts of the input. This approach only works when practitioners know a priori symmetries of the task and can manually c... | Meta-Learning Symmetries by Reparameterization |
d3495200 | Learning tasks on source code (i.e., formal languages) have been considered recently, but most work has tried to transfer natural language methods and does not capitalize on the unique opportunities offered by code's known syntax. For example, long-range dependencies induced by using the same variable or function in di... | LEARNING TO REPRESENT PROGRAMS WITH GRAPHS |
d498451 | We present a novel layerwise optimization algorithm for the learning objective of Piecewise-Linear Convolutional Neural Networks (PL-CNNs), a large class of convolutional neural networks. Specifically, PL-CNNs employ piecewise linear non-linearities such as the commonly used ReLU and max-pool, and an SVM classifier as ... | TRUSTING SVM FOR PIECEWISE LINEAR CNNS |
d209318411 | Standard variational lower bounds used to train latent variable models produce biased estimates of most quantities of interest. We introduce an unbiased estimator of the log marginal likelihood and its gradients for latent variable models based on randomized truncation of infinite series. If parameterized by an encoder... | SUMO: UNBIASED ESTIMATION OF LOG MARGINAL PROBABILITY FOR LATENT VARIABLE MODELS |
d248085789 | Self-supervised learning (SSL) is capable of learning remarkable representations from centrally available data. Recent works further implement federated learning with SSL to learn from rapidly growing decentralized unlabeled images (e.g., from cameras and phones), often resulted from privacy constraints. Extensive atte... | DIVERGENCE-AWARE FEDERATED SELF-SUPERVISED LEARNING |
d21473141 | We consider the learning of algorithmic tasks by mere observation of input-output pairs. Rather than studying this as a black-box discrete regression problem with no assumption whatsoever on the input-output mapping, we concentrate on tasks that are amenable to the principle of divide and conquer, and study what are it... | Divide and Conquer Networks |
d52895739 | To make deep neural networks feasible in resource-constrained environments (such as mobile devices), it is beneficial to quantize models by using low-precision weights. One common technique for quantizing neural networks is the straight-through gradient method, which enables back-propagation through the quantization ma... | ProxQuant: Quantized Neural Networks via Proximal Operators |
d56177829 | Efficient exploration remains a major challenge for reinforcement learning. One reason is that the variability of the returns often depends on the current state and action, and is therefore heteroscedastic. Classical exploration strategies such as upper confidence bound algorithms and Thompson sampling fail to appropri... | INFORMATION-DIRECTED EXPLORATION FOR DEEP REINFORCEMENT LEARNING |
d246634839 | Online learning via Bayes' theorem allows new data to be continuously integrated into an agent's current beliefs. However, a naive application of Bayesian methods in non-stationary environments leads to slow adaptation and results in state estimates that may converge confidently to the wrong parameter value. A common s... | BAM: BAYES WITH ADAPTIVE MEMORY |
d261076491 | Recently there has been a significant surge in multimodal learning in terms of both image-to-text and text-to-image generation. However, the success is typically limited to English, leaving other languages largely behind. Building a competitive counterpart in other languages is highly challenging due to the low-resourc... | Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages |
d257219618 | There is a rising interest in further exploring the zero-shot learning potential of large pre-trained language models (PLMs). A new paradigm called data-generationbased zero-shot learning has achieved impressive success. In this paradigm, the synthesized data from the PLM acts as the carrier of knowledge, which is used... | SELF-GUIDED NOISE-FREE DATA GENERATION FOR EFFICIENT ZERO-SHOT LEARNING |
d202565422 | Video prediction models combined with planning algorithms have shown promise in enabling robots to learn to perform many vision-based tasks through only selfsupervision, reaching novel goals in cluttered scenes with unseen objects. However, due to the compounding uncertainty in long horizon video prediction and poor sc... | HIERARCHICAL FORESIGHT: SELF-SUPERVISED LEARNING OF LONG-HORIZON TASKS VIA VISUAL SUBGOAL GENERATION |
d240354511 | Recently, self-supervised learning has attracted great attention, since it only requires unlabeled data for model training. Contrastive learning is one popular method for self-supervised learning and has achieved promising empirical performance. However, the theoretical understanding of its generalization ability is st... | TOWARDS THE GENERALIZATION OF CONTRASTIVE SELF-SUPERVISED LEARNING |
d85504763 | Mathematical reasoning-a core ability within human intelligence-presents some unique challenges as a domain: we do not come to understand and solve mathematical problems primarily on the back of experience and evidence, but on the basis of inferring, learning, and exploiting laws, axioms, and symbol manipulation rules.... | ANALYSING MATHEMATICAL REASONING ABILITIES OF NEURAL MODELS |
d237532482 | Estimating the performance of a machine learning system is a longstanding challenge in artificial intelligence research.Today, this challenge is especially relevant given the emergence of systems which appear to increasingly outperform human beings.In some cases, this "superhuman" performance is readily demonstrated; f... | Humanly Certifying Superhuman Classifiers |
d253107515 | The decentralized Federated Learning (FL) setting avoids the role of a potentially unreliable or untrustworthy central host by utilizing groups of clients to collaboratively train a model via localized training and model/gradient sharing. Most existing decentralized FL algorithms require synchronization of client model... | SWIFT: RAPID DECENTRALIZED FEDERATED LEARNING VIA WAIT-FREE MODEL COMMUNICATION |
d252596112 | A Transformer-based deep direct sampling method is proposed for electrical impedance tomography, a well-known severely ill-posed nonlinear boundary value inverse problem. A real-time reconstruction is achieved by evaluating the learned inverse operator between carefully designed data and the reconstructed images. An ef... | TRANSFORMER MEETS BOUNDARY VALUE INVERSE PROBLEMS |
d239016684 | We provide nearly optimal algorithms for online facility location (OFL) with predictions. In OFL, n demand points arrive in order and the algorithm must irrevocably assign each demand point to an open facility upon its arrival. The objective is to minimize the total connection costs from demand points to assigned facil... | ONLINE FACILITY LOCATION WITH PREDICTIONS |
d259165262 | Recent deep neural networks (DNNs) have come to rely on vast amounts of training data, providing an opportunity for malicious attackers to exploit and contaminate the data to carry out backdoor attacks. These attacks significantly undermine the reliability of DNNs. However, existing backdoor attack methods make unreali... | Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios |
d256389660 | We study a security threat to adversarial multi-armed bandits, in which an attacker perturbs the loss or reward signal to control the behavior of the victim bandit player. We show that the attacker is able to mislead any no-regret adversarial bandit algorithm into selecting a suboptimal target arm in every but sublinea... | ADVERSARIAL ATTACKS ON ADVERSARIAL BANDITS |
d52086172 | We extend Stochastic Gradient Variational Bayes to perform posterior inference for the weights of Stick-Breaking processes. This development allows us to define a Stick-Breaking Variational Autoencoder (SB-VAE), a Bayesian nonparametric version of the variational autoencoder that has a latent representation with stocha... | STICK-BREAKING VARIATIONAL AUTOENCODERS |
d257079127 | Human motion transfer aims to transfer motions from a target dynamic person to a source static one for motion synthesis. An accurate matching between the source person and the target motion in both large and subtle motion changes is vital for improving the transferred motion quality. In this paper, we propose Human Mot... | HUMAN MOTIONFORMER: TRANSFERRING HUMAN MOTIONS WITH VISION TRANSFORMERS |
d245144606 | Neural memory enables fast adaptation to new tasks with just a few training samples. Existing memory models store features only from the single last layer, which does not generalize well in presence of a domain shift between training and test distributions. Rather than relying on a flat memory, we propose a hierarchica... | HIERARCHICAL VARIATIONAL MEMORY FOR FEW-SHOT LEARNING ACROSS DOMAINS |
d259129342 | Causal inference is one of the hallmarks of human intelligence. While the field of CausalNLP has attracted much interest in the recent years, existing causal inference datasets in NLP primarily rely on discovering causality from empirical knowledge (e.g. commonsense knowledge). In this work, we propose the first benchm... | Can Large Language Models Infer Causation from Correlation? |
d52901998 | For autonomous agents to successfully operate in the real world, the ability to anticipate future scene states is a key competence. In real-world scenarios, future states become increasingly uncertain and multi-modal, particularly on long time horizons. Dropout based Bayesian inference provides a computationally tracta... | BAYESIAN PREDICTION OF FUTURE STREET SCENES USING SYNTHETIC LIKELIHOODS |
d235313715 | Potential games are arguably one of the most important and widely studied classes of normal form games. They define the archetypal setting of multi-agent coordination as all agent utilities are perfectly aligned with each other via a common potential function. Can this intuitive framework be transplanted in the setting... | Global Convergence of Multi-Agent Policy Gradient in Markov Potential Games |
d237532682 | We present an empirical study of scaling properties of encoder-decoder Transformer models used in neural machine translation (NMT). We show that cross-entropy loss as a function of model size follows a certain scaling law. Specifically (i) We propose a formula which describes the scaling behavior of cross-entropy loss ... | Scaling Laws for Neural Machine Translation |
d3303573 | Trust region methods, such as TRPO, are often used to stabilize policy optimization algorithms in reinforcement learning (RL). While current trust region strategies are effective for continuous control, they typically require a prohibitively large amount of on-policy interaction with the environment. To address this pr... | Trust-PCL: An Off-Policy Trust Region Method for Continuous Control |
d256697500 | Generalizable manipulation skills, which can be composed to tackle longhorizon and complex daily chores, are one of the cornerstones of Embodied AI. However, existing benchmarks, mostly composed of a suite of simulatable environments, are insufficient to push cutting-edge research works because they lack object-level t... | MANISKILL2: A UNIFIED BENCHMARK FOR GENERALIZABLE MANIPULATION SKILLS |
d12256925 | We describe a simple scheme that allows an agent to learn about its environment in an unsupervised manner. Our scheme pits two versions of the same agent, Alice and Bob, against one another. Alice proposes a task for Bob to complete; and then Bob attempts to complete the task. In this work we will focus on two kinds of... | Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play |
d108367114 | The goal of program synthesis is to automatically generate programs in a particular language from corresponding specifications, e.g. input-output behavior. Many current approaches achieve impressive results after training on randomly generated I/O examples in limited domain-specific languages (DSLs), as with string tra... | SYNTHETIC DATASETS FOR NEURAL PROGRAM SYNTHESIS |
d222140630 | This paper frames a general prediction system as an observer traveling around a continuous space, measuring values at some locations, and predicting them at others. The observer is completely agnostic about any particular task being solved; it cares only about measurement locations and their values. This perspective le... | THE TRAVELING OBSERVER MODEL: MULTI-TASK LEARNING THROUGH SPATIAL VARIABLE EMBEDDINGS |
d210903109 | We consider the problem of learning control policies that optimize a reward function while satisfying constraints due to considerations of safety, fairness, or other costs. We propose a new algorithm, Projection-Based Constrained Policy Optimization (PCPO). This is an iterative method for optimizing policies in a two-s... | PROJECTION-BASED CONSTRAINED POLICY OPTIMIZATION |
d252683127 | Influence functions estimate effect of individual data points on predictions of the model on test data and were adapted to deep learning in Koh and Liang [2017]. They have been used for detecting data poisoning, detecting helpful and harmful examples, influence of groups of datapoints, etc. Recently, Ilyas et al. [2022... | Understanding Influence Functions and Datamodels via Harmonic Analysis |
d3517431 | The state-of-the-art (SOTA) for mixed precision training is dominated by variants of low precision floating point operations, and in particular FP16 accumulating into FP32 . On the other hand, while a lot of research has also happened in the domain of low and mixed-precision Integer training, these works either present... | MIXED PRECISION TRAINING OF CONVOLUTIONAL NEURAL NETWORKS USING INTEGER OPERATIONS |
d244117789 | In distribution compression, one aims to accurately summarize a probability distribution P using a small number of representative points. Near-optimal thinning procedures achieve this goal by sampling n points from a Markov chain and identifying √ n points with O(1/ √ n) discrepancy to P. Unfortunately, these algorithm... | DISTRIBUTION COMPRESSION IN NEAR-LINEAR TIME |
d222381644 | Many recent methods for unsupervised representation learning involve training models to be invariant to different "views," or transformed versions of an input. However, designing these views requires considerable human expertise and experimentation, hindering widespread adoption of unsupervised representation learning ... | VIEWMAKER NETWORKS: LEARNING VIEWS FOR UNSUPERVISED REPRESENTATION LEARNING |
d263608162 | Prompt tuning in natural language processing (NLP) has become an increasingly popular method for adapting large language models to specific tasks.However, the transferability of these prompts, especially continuous prompts, between different models remains a challenge.In this work, we propose a zero-shot continuous pro... | Zero-Shot Continuous Prompt Transfer: Generalizing Task Semantics Across Language Models |
d233004331 | Pooling is a critical operation in convolutional neural networks for increasing receptive fields and improving robustness to input variations. Most existing pooling operations downsample the feature maps, which is a lossy process. Moreover, they are not invertible: upsampling a downscaled feature map can not recover th... | LIFTPOOL: BIDIRECTIONAL CONVNET POOLING |
d261100593 | In recommender systems, users always choose the favorite items to rate, which leads to data missing not at random and poses a great challenge for unbiased evaluation and learning of prediction models. Currently, the doubly robust (DR) methods have been widely studied and demonstrate superior performance. However, in th... | STABLEDR: STABILIZED DOUBLY ROBUST LEARNING FOR RECOMMENDATION ON DATA MISSING NOT AT RANDOM |
d228083457 | Recent research has shown remarkable success in revealing "steering" directions in the latent spaces of pre-trained GANs. These directions correspond to semantically meaningful image transformations (e.g., shift, zoom, color manipulations), and have similar interpretable effects across all categories that the GAN can g... | GAN "STEERABILITY" WITHOUT OPTIMIZATION |
d53941707 | We present a large-scale empirical study of catastrophic forgetting (CF) in modern Deep Neural Network (DNN) models that perform sequential (or: incremental) learning. A new experimental protocol is proposed that enforces typical constraints encountered in application scenarios. As the investigation is empirical, we ev... | A COMPREHENSIVE, APPLICATION-ORIENTED STUDY OF CATASTROPHIC FORGETTING IN DNNS |
d261276856 | Diffusion models have demonstrated impressive generative capabilities, but their exposure bias problem, described as the input mismatch between training and sampling, lacks in-depth exploration.In this paper, we systematically investigate the exposure bias problem in diffusion models by first analytically modelling the... | ELUCIDATING THE EXPOSURE BIAS IN DIFFUSION MODELS |
d253265269 | Deep learning vision systems are widely deployed across applications where reliability is critical. However, even today's best models can fail to recognize an object when its pose, lighting, or background varies. While existing benchmarks surface examples that are challenging for models, they do not explain why such mi... | ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations |
d59553475 | We propose a method to incrementally learn an embedding space over the domain of network architectures, to enable the careful selection of architectures for evaluation during compressed architecture search. Given a teacher network, we search for a compressed network architecture by using Bayesian Optimization (BO) with... | LEARNABLE EMBEDDING SPACE FOR EFFICIENT NEURAL ARCHITECTURE COMPRESSION |
d253098810 | This paper investigates when one can efficiently recover an approximate Nash Equilibrium (NE) in offline congestion games. The existing dataset coverage assumption in offline general-sum games inevitably incurs a dependency on the number of actions, which can be exponentially large in congestion games. We consider th... | Offline congestion games: How feedback type affects data coverage requirement |
d263830421 | Large Language Models (LLMs) with billions of parameters have drastically transformed AI applications.However, their demanding computation during inference has raised significant challenges for deployment on resource-constrained devices.Despite recent trends favoring alternative activation functions such as GELU or SiL... | ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models |
d244488490 | Point cloud obtained from 3D scanning is often sparse, noisy, and irregular. To cope with these issues, recent studies have been separately conducted to densify, denoise, and complete inaccurate point cloud. In this paper, we advocate that jointly solving these tasks leads to significant improvement for point cloud rec... | DEEP POINT CLOUD RECONSTRUCTION |
d207847719 | Graph Convolutional Networks (GCNs) have recently been shown to be quite successful in modeling graph-structured data. However, the primary focus has been on handling simple undirected graphs. Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated wi... | COMPOSITION-BASED MULTI-RELATIONAL GRAPH CONVOLUTIONAL NETWORKS |
d254070084 | A number of competing hypotheses have been proposed to explain why small-batch Stochastic Gradient Descent (SGD) leads to improved generalization over the full-batch regime, with recent work crediting the implicit regularization of various quantities throughout training. However, to date, empirical evidence assessing t... | Disentangling the Mechanisms Behind Implicit Regularization in SGD |
d249395201 | Generative adversarial networks (GANs) are challenging to train stably, and a promising remedy of injecting instance noise into the discriminator input has not been very effective in practice. In this paper, we propose Diffusion-GAN, a novel GAN framework that leverages a forward diffusion chain to generate Gaussianmix... | DIFFUSION-GAN: TRAINING GANS WITH DIFFUSION |
d234358843 | Recent discoveries on neural network pruning reveal that, with a carefully chosen layerwise sparsity, a simple magnitude-based pruning achieves state-of-the-art tradeoff between sparsity and performance. However, without a clear consensus on "how to choose," the layerwise sparsities are mostly selected algorithm-byalgo... | LAYER-ADAPTIVE SPARSITY FOR THE MAGNITUDE-BASED PRUNING |
d20285896 | In this paper, we present a systematic study on GANs with categorical discriminator, especially their impact on the optimization scheme of the generator. We derive class-aware gradients and cross-entropy decomposition, to theoretically reveal how they help GAN training and the inherent problems in previous models. Base... | Activation Maximization Generative Adversarial Nets |
d221971208 | Transfer of pre-trained representations can improve sample efficiency and reduce computational requirements for new tasks. However, representations used for transfer are usually generic, and are not tailored to a particular distribution of downstream tasks. We explore the use of expert representations for transfer with... | Scalable Transfer Learning with Expert Models |
d252693109 | A multitude of work has shown that machine learning-based medical diagnosis systems can be biased against certain subgroups of people. This has motivated a growing number of bias mitigation algorithms that aim to address fairness issues in machine learning. However, it is difficult to compare their effectiveness in med... | MEDFAIR: BENCHMARKING FAIRNESS FOR MEDICAL IMAGING |
d423406 | We use reinforcement learning to learn tree-structured neural networks for computing representations of natural language sentences. In contrast with prior work on tree-structured models in which the trees are either provided as input or predicted using supervision from explicit treebank annotations, the tree structures... | LEARNING TO COMPOSE WORDS INTO SENTENCES WITH REINFORCEMENT LEARNING |
d253553242 | Finding equilibria via gradient play in competitive multi-agent games has been attracting a growing amount of attention in recent years, with emphasis on designing efficient strategies where the agents operate in a decentralized and symmetric manner with guaranteed convergence. While significant efforts have been made ... | Asynchronous Gradient Play in Zero-Sum Multi-agent Games |
d5071138 | We propose DuoRC, a novel dataset for Reading Comprehension (RC) that motivates several new challenges for neural approaches in language understanding beyond those offered by existing RC datasets. DuoRC contains 186,089 unique questionanswer pairs created from a collection of 7680 pairs of movie plots where each pair i... | DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension |
d264820183 | Recently video generation has achieved substantial progress with realistic results.Nevertheless, existing AI-generated videos are usually very short clips ("shotlevel") depicting a single scene.To deliver a coherent long video ("story-level"), it is desirable to have creative transition and prediction effects across di... | SEINE: SHORT-TO-LONG VIDEO DIFFUSION MODEL FOR GENERATIVE TRANSITION AND PREDICTION |
d3698524 | We present a general-purpose method to train Markov chain Monte Carlo kernels, parameterized by deep neural networks, that converge and mix quickly to their target distribution. Our method generalizes Hamiltonian Monte Carlo and is trained to maximize expected squared jumped distance, a proxy for mixing speed. We demon... | GENERALIZING HAMILTONIAN MONTE CARLO WITH NEURAL NETWORKS |
d221836662 | We prove that the reproducing kernel Hilbert spaces (RKHS) of a deep neural tangent kernel and the Laplace kernel include the same set of functions, when both kernels are restricted to the sphere S d−1 . Additionally, we prove that the exponential power kernel with a smaller power (making the kernel more non-smooth) le... | Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS |
d264306002 | We present a new technique to enhance the robustness of imitation learning methods by generating corrective data to account for compounding errors and disturbances.While existing methods rely on interactive expert labeling, additional offline datasets, or domain-specific invariances, our approach requires minimal addit... | CCIL: CONTINUITY-BASED DATA AUGMENTATION FOR CORRECTIVE IMITATION LEARNING |
d211027382 | As gradual typing becomes increasingly popular in languages like Python and TypeScript, there is a growing need to infer type annotations automatically. While type annotations help with tasks like code completion and static error catching, these annotations cannot be fully determined by compilers and are tedious to ann... | LAMBDANET: PROBABILISTIC TYPE INFERENCE USING GRAPH NEURAL NETWORKS |
d3482308 | In this paper we investigate the family of functions representable by deep neural networks (DNN) with rectified linear units (ReLU). We give the first-ever polynomial time (in the size of data) algorithm to train to global optimality a ReLU DNN with one hidden layer, assuming the input dimension and number of nodes of ... | Understanding Deep Neural Networks with Rectified Linear Units |
d7070838 | In implicit models, one often interpolates between sampled points in latent space. As we show in this paper, care needs to be taken to match-up the distributional assumptions on code vectors with the geometry of the interpolating paths. Otherwise, typical assumptions about the quality and semantics of in-between points... | SEMANTIC INTERPOLATION IN IMPLICIT MODELS |
d262054014 | Fine-tuning (via methods such as instruction-tuning or reinforcement learning from human feedback) is a crucial step in training language models to robustly carry out tasks of interest.However, we lack a systematic understanding of the effects of fine-tuning, particularly on tasks outside the narrow fine-tuning distrib... | UNDERSTANDING CATASTROPHIC FORGETTING IN LANGUAGE MODELS VIA IMPLICIT INFERENCE |
d251648935 | As machine learning algorithms are deployed ubiquitously to a variety of domains, it is imperative to make these often black-box models transparent. Several recent works explain black-box models by capturing the most influential features for prediction per instance; such explanation methods are univariate, as they char... | EXPLANATIONS OF BLACK-BOX MODELS BASED ON DIRECTIONAL FEATURE INTERACTIONS |
d238744324 | We consider the problem of using expert data with unobserved confounders for imitation and reinforcement learning. We begin by defining the problem of learning from confounded expert data in a contextual MDP setup. We analyze the limitations of learning from such data with and without external reward, and propose an ad... | ON COVARIATE SHIFT OF LATENT CONFOUNDERS IN IMITATION AND REINFORCEMENT LEARNING |
d253080775 | As large language models (LLMs) grow larger and more sophisticated, assessing their "reasoning" capabilities in natural language grows more challenging. Recent question answering (QA) benchmarks that attempt to assess reasoning are often limited by a narrow scope of covered situations and subject matters. We introduce ... | WIKIWHY: ANSWERING AND EXPLAINING CAUSE-AND-EFFECT QUESTIONS |
d67856222 | Batch Normalization (BN) is a common technique used to speed-up and stabilize training. On the other hand, the learnable parameters of BN are commonly used in conditional Generative Adversarial Networks (cGANs) for representing classspecific information using conditional Batch Normalization (cBN). In this paper we prop... | WHITENING AND COLORING BATCH TRANSFORM FOR GANS |
d246863450 | Learning in strategy games (e.g. StarCraft, poker) requires the discovery of diverse policies. This is often achieved by iteratively training new policies against existing ones, growing a policy population that is robust to exploit. This iterative approach suffers from two issues in real-world games: a) under finite bu... | NEUPL: NEURAL POPULATION LEARNING |
d247593929 | Federated learning (FL) provides a distributed learning framework for multiple participants to collaborate learning without sharing raw data. In many practical FL scenarios, participants have heterogeneous resources due to disparities in hardware and inference dynamics that require quickly loading models of different s... | EFFICIENT SPLIT-MIX FEDERATED LEARNING FOR ON-DEMAND AND IN-SITU CUSTOMIZATION |
d252918639 | Model compression is vital to the deployment of deep learning on edge devices. Low precision representations, achieved via quantization of weights and activations, can reduce inference time and memory requirements. However, quantifying and predicting the response of a model to the changes associated with this procedure... | FIT: A METRIC FOR MODEL SENSITIVITY |
d219708206 | The behavior of many dynamical systems follow complex, yet still unknown partial differential equations (PDEs). While several machine learning methods have been proposed to learn PDEs directly from data, previous methods are limited to discretetime approximations or make the limiting assumption of the observations arri... | Learning continuous-time PDEs from sparse data with graph neural networks |
d239050236 | Schrödinger Bridge (SB) is an entropy-regularized optimal transport problem that has received increasing attention in deep generative modeling for its mathematical flexibility compared to the Scored-based Generative Model (SGM). However, it remains unclear whether the optimization principle of SB relates to the modern ... | LIKELIHOOD TRAINING OF SCHRÖDINGER BRIDGE USING FORWARD-BACKWARD SDES THEORY |
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