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d254044338 | Over-parameterized residual networks are amongst the most successful convolutional neural architectures for image processing. Here we study their properties through their Gaussian Process and Neural Tangent kernels. We derive explicit formulas for these kernels, analyze their spectra and provide bounds on their implied... | A KERNEL PERSPECTIVE OF SKIP CONNECTIONS IN CONVOLUTIONAL NETWORKS |
d247741267 | A variety of methods exist to explain image classification models. However, it remains unclear whether they provide any benefit to users over simply comparing various inputs and the model's respective predictions. We conducted a user study (N=240) to test how such a baseline explanation technique performs against conce... | DO USERS BENEFIT FROM INTERPRETABLE VISION? A USER STUDY, BASELINE, AND DATASET |
d254636478 | Figure 1: Generated video frames from the proposed model trained on Countryside, YouTube Driving (Zhang et al., 2022), and SkyTimelapse (Xiong et al., 2018) (from top to bottom).ABSTRACT Video generation requires synthesizing consistent and persistent frames with dynamic content over time. This work investigates modeli... | TOWARDS SMOOTH VIDEO COMPOSITION |
d252668503 | We consider the problem of optimization of deep learning models with smooth activation functions. While there exist influential results on the problem from the "near initialization" perspective, we shed considerable new light on the problem. In particular, we make two key technical contributions for such models with L ... | Restricted Strong Convexity of Deep Learning Models with Smooth Activations |
d263828899 | A critical barrier to learning an accurate decision rule for outlier detection is the scarcity of outlier data.As such, practitioners often turn to the use of similar but imperfect outlier data from which they might transfer information to the target outlier detection task.Despite the recent empirical success of transf... | TIGHT RATES IN SUPERVISED OUTLIER TRANSFER LEARNING |
d238634210 | When deployed in the real world, machine learning models inevitably encounter changes in the data distribution, and certain-but not all-distribution shifts could result in significant performance degradation. In practice, it may make sense to ignore benign shifts, under which the performance of a deployed model does no... | Tracking the risk of a deployed model and detecting harmful distribution shifts |
d239016463 | Modern deep learning based classifiers show very high accuracy on test data but this does not provide sufficient guarantees for safe deployment, especially in highstake AI applications such as medical diagnosis. Usually, predictions are obtained without a reliable uncertainty estimate or a formal guarantee. Conformal p... | LEARNING OPTIMAL CONFORMAL CLASSIFIERS |
d210064343 | Adversarial training is one of the most popular ways to learn robust models but is usually attack-dependent and time costly. In this paper, we propose the MACER algorithm, which learns robust models without using adversarial training but performs better than all existing provable l 2 -defenses. Recent work(Cohen et al.... | MACER: ATTACK-FREE AND SCALABLE ROBUST TRAINING VIA MAXIMIZING CERTIFIED RADIUS |
d221340643 | Recent research has proposed neural architectures for solving combinatorial problems in structured output spaces. In many such problems, there may exist multiple solutions for a given input, e.g. a partially filled Sudoku puzzle may have many completions satisfying all constraints. Further, we are often interested in f... | Neural Learning of One-of-Many Solutions for Combinatorial Problems in Structured Output Spaces |
d3333039 | Neural networks are commonly used as models for classification for a wide variety of tasks. Typically, a learned affine transformation is placed at the end of such models, yielding a per-class value used for classification. This classifier can have a vast number of parameters, which grows linearly with the number of po... | FIX YOUR CLASSIFIER: THE MARGINAL VALUE OF TRAINING THE LAST WEIGHT LAYER |
d258967501 | Methods for carefully selecting or generating a small set of training data to learn from, i.e., data pruning, coreset selection, and data distillation, have been shown to be effective in reducing the ever-increasing cost of training neural networks. Behind this success are rigorously designed strategies for identifying... | Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning |
d240354406 | Learning on graphs has attracted significant attention in the learning community due to numerous real-world applications. In particular, graph neural networks (GNNs), which take numerical node features and graph structure as inputs, have been shown to achieve state-of-the-art performance on various graph-related learni... | NODE FEATURE EXTRACTION BY SELF-SUPERVISED MULTI-SCALE NEIGHBORHOOD PREDICTION |
d257365017 | Deep neural networks can learn powerful prior probability models for images, as evidenced by the high-quality generations obtained with recent score-based diffusion methods. But the means by which these networks capture complex global statistical structure, apparently without suffering from the curse of dimensionality,... | LEARNING MULTI-SCALE LOCAL CONDITIONAL PROBABILITY MODELS OF IMAGES |
d257232821 | Category-level articulated object pose estimation aims to estimate a hierarchy of articulation-aware object poses of an unseen articulated object from a known category. To reduce the heavy annotations needed for supervised learning methods, we present a novel self-supervised strategy that solves this problem without an... | SELF-SUPERVISED CATEGORY-LEVEL ARTICULATED OBJECT POSE ESTIMATION WITH PART-LEVEL SE(3) EQUIVARIANCE |
d235614336 | In high-dimensional state spaces, the usefulness of Reinforcement Learning (RL) is limited by the problem of exploration. This issue has been addressed using potential-based reward shaping (PB-RS) previously. In the present work, we introduce Final-Volume-Preserving Reward Shaping (FV-RS). FV-RS relaxes the strict opti... | PLAN-BASED RELAXED REWARD SHAPING FOR GOAL-DIRECTED TASKS |
d246240517 | The generalization capacity of various machine learning models exhibits different phenomena in the under-and over-parameterized regimes. In this paper, we focus on regression models such as feature regression and kernel regression and analyze a generalized weighted least-squares optimization method for computational le... | A GENERALIZED WEIGHTED OPTIMIZATION METHOD FOR COMPUTATIONAL LEARNING AND INVERSION |
d260164542 | When writing programs, people have the ability to tackle a new complex task by decomposing it into smaller and more familiar subtasks. While it is difficult to measure whether neural program synthesis methods have similar capabilities, we can measure whether they compositionally generalize, that is, whether a model tha... | ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis |
d256104987 | Marginal-based methods achieve promising performance in the synthetic data competition hosted by the National Institute of Standards and Technology (NIST). To deal with high-dimensional data, the distribution of synthetic data is represented by a probabilistic graphical model (e.g., a Bayesian network), while the raw d... | STATISTICAL THEORY OF DIFFERENTIALLY PRIVATE MARGINAL-BASED DATA SYNTHESIS ALGORITHMS |
d12663716 | PixelCNNs are a recently proposed class of powerful generative models with tractable likelihood. Here we discuss our implementation of PixelCNNs which we make available at https://github.com/openai/pixel-cnn. Our implementation contains a number of modifications to the original model that both simplify its structure an... | PIXELCNN++: IMPROVING THE PIXELCNN WITH DISCRETIZED LOGISTIC MIXTURE LIKELIHOOD AND OTHER MODIFICATIONS |
d204575696 | This paper establishes rates of universal approximation for the shallow neural tangent kernel (NTK): network weights are only allowed microscopic changes from random initialization, which entails that activations are mostly unchanged, and the network is nearly equivalent to its linearization. Concretely, the paper has ... | Neural tangent kernels, transportation mappings, and universal approximation |
d252682977 | Many real-world dynamical systems are associated with first integrals (a.k.a. invariant quantities), which are quantities that remain unchanged over time. The discovery and understanding of first integrals are fundamental and important topics both in the natural sciences and in industrial applications. First integrals ... | FINDE: NEURAL DIFFERENTIAL EQUATIONS FOR FINDING AND PRESERVING INVARIANT QUANTITIES |
d3687922 | We introduce a new memory architecture for navigation in previously unseen environments, inspired by landmark-based navigation in animals. The proposed semiparametric topological memory (SPTM) consists of a (non-parametric) graph with nodes corresponding to locations in the environment and a (parametric) deep network c... | SEMI-PARAMETRIC TOPOLOGICAL MEMORY FOR NAVIGATION |
d238743980 | In this paper, we conjecture that if the permutation invariance of neural networks is taken into account, SGD solutions will likely have no barrier in the linear interpolation between them. Although it is a bold conjecture, we show how extensive empirical attempts fall short of refuting it. We further provide a prelimi... | The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks |
d220363813 | In learning-assisted theorem proving, one of the most critical challenges is to generalize to theorems unlike those seen at training time. In this paper, we introduce INT, an INequality Theorem proving benchmark, specifically designed to test agents' generalization ability. INT is based on a procedure for generating th... | INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving |
d247618909 | Inspired by the success of self-supervised autoregressive representation learning in natural language (GPT and its variants), and advances in recent visual architecture design with Vision Transformers (ViTs), in this paper, we explore the effect various design choices have on the success of applying such training strat... | SELF-SUPERVISION THROUGH RANDOM SEGMENTS WITH AUTOREGRESSIVE CODING (RANDSAC) |
d256616217 | Due to the significant computational challenge of training large-scale graph neural networks (GNNs), various sparse learning techniques have been exploited to reduce memory and storage costs. Examples include graph sparsification that samples a subgraph to reduce the amount of data aggregation and model sparsification ... | JOINT EDGE-MODEL SPARSE LEARNING IS PROVABLY EFFICIENT FOR GRAPH NEURAL NETWORKS |
d253734944 | Real-world machine learning applications often involve deploying neural networks to domains that are not seen in the training time. Hence, we need to understand the extrapolation of nonlinear models-under what conditions on the distributions and function class, models can be guaranteed to extrapolate to new test distri... | First Steps Toward Understanding the Extrapolation of Nonlinear Models to Unseen Domains |
d233004517 | Modern neural networks excel at image classification, yet they remain vulnerable to common image corruptions such as blur, speckle noise or fog. Recent methods that focus on this problem, such as AugMix and DeepAugment, introduce defenses that operate in expectation over a distribution of image corruptions. In contrast... | DEFENDING AGAINST IMAGE CORRUPTIONS THROUGH ADVERSARIAL AUGMENTATIONS |
d203593240 | Learning to disentangle the hidden factors of variations within a set of observations is a key task for artificial intelligence. We present a unified formulation for class and content disentanglement and use it to illustrate the limitations of current methods. We therefore introduce LORD, a novel method based on Latent... | DEMYSTIFYING INTER-CLASS DISENTANGLEMENT |
d264935097 | We propose a new approach for generative modeling based on training a neural network to be idempotent.An idempotent operator is one that can be applied sequentially without changing the result beyond the initial application, namely f (f (z)) = f (z).The proposed model f is trained to map a source distribution (e.g, Gau... | IDEMPOTENT GENERATIVE NETWORK |
d12167053 | Adversarial training provides a means of regularizing supervised learning algorithms while virtual adversarial training is able to extend supervised learning algorithms to the semi-supervised setting. However, both methods require making small perturbations to numerous entries of the input vector, which is inappropriat... | ADVERSARIAL TRAINING METHODS FOR SEMI-SUPERVISED TEXT CLASSIFICATION |
d231942635 | Two-party split learning is a popular technique for learning a model across feature-partitioned data. In this work, we explore whether it is possible for one party to steal the private label information from the other party during split training, and whether there are methods that can protect against such attacks. Spec... | Label Leakage and Protection in Two-party Split Learning |
d255096726 | For small training set sizes P , the generalization error of wide neural networks is well-approximated by the error of an infinite width neural network (NN), either in the kernel or mean-field/feature-learning regime. However, after a critical sample size P * , we empirically find the finite-width network generalizatio... | THE ONSET OF VARIANCE-LIMITED BEHAVIOR FOR NETWORKS IN THE LAZY AND RICH REGIMES |
d3783953 | Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry. Here we introduce a powerful new approach for learning generative models over graphs, which can capture both ... | Learning Deep Generative Models of Graphs |
d261706069 | Offline reinforcement learning (RL) holds promise as a means to learn high-reward policies from a static dataset, without the need for further environment interactions. However, a key challenge in offline RL lies in effectively stitching portions of suboptimal trajectories from the static dataset while avoiding extrapo... | Reasoning with Latent Diffusion in Offline Reinforcement Learning |
d222133956 | We prove a non-asymptotic distribution-independent lower bound for the expected mean squared generalization error caused by label noise in ridgeless linear regression. Our lower bound generalizes a similar known result to the overparameterized (interpolating) regime. In contrast to most previous works, our analysis app... | ON THE UNIVERSALITY OF THE DOUBLE DESCENT PEAK IN RIDGELESS REGRESSION |
d259108565 | In-context learning is one of the surprising and useful features of large language models. How it works is an active area of research. Recently, stylized meta-learning-like setups have been devised that train these models on a sequence of input-output pairs (x, f (x)) from a function class using the language modeling l... | In-Context Learning through the Bayesian Prism |
d252668316 | We develop fast distribution-free conformal prediction algorithms for obtaining multivalid coverage on exchangeable data in the batch setting. Multivalid coverage guarantees are stronger than marginal coverage guarantees in two ways: (1) They hold even conditional on group membership-that is, the target coverage level ... | Batch Multivalid Conformal Prediction |
d239016008 | Unsupervised feature learning often finds low-dimensional embeddings that capture the structure of complex data. For tasks for which prior expert topological knowledge is available, incorporating this into the learned representation may lead to higher quality embeddings. For example, this may help one to embed the data... | TOPOLOGICALLY REGULARIZED DATA EMBEDDINGS |
d209444460 | Spectral embedding is a popular technique for the representation of graph data. Several regularization techniques have been proposed to improve the quality of the embedding with respect to downstream tasks like clustering. In this paper, we explain on a simple block model the impact of the complete graph regularization... | SPECTRAL EMBEDDING OF REGULARIZED BLOCK MODELS |
d263605817 | Multi-modal learning has become increasingly popular due to its ability to leverage information from different data sources (e.g., text and images) to improve the model performance.Recently, CLIP has emerged as an effective approach that employs vision-language contrastive pretraining to learn joint image and text repr... | Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP |
d258352594 | We present Analogical Networks, a model that encodes domain knowledge explicitly, in a collection of structured labelled 3D scenes, in addition to implicitly, as model parameters, and segments 3D object scenes with analogical reasoning: instead of mapping a scene to part segments directly, our model first retrieves rel... | ANALOGY-FORMING TRANSFORMERS FOR FEW-SHOT 3D PARSING |
d53113673 | We propose a neural machine-reading model that constructs dynamic knowledge graphs from procedural text. It builds these graphs recurrently for each step of the described procedure, and uses them to track the evolving states of participant entities. We harness and extend a recently proposed machine reading comprehensio... | BUILDING DYNAMIC KNOWLEDGE GRAPHS FROM TEXT USING MACHINE READING COMPREHENSION |
d253018859 | A common approach to transfer learning under distribution shift is to fine-tune the last few layers of a pre-trained model, preserving learned features while also adapting to the new task. This paper shows that in such settings, selectively fine-tuning a subset of layers (which we term surgical fine-tuning) matches or ... | SURGICAL FINE-TUNING IMPROVES ADAPTATION TO DISTRIBUTION SHIFTS |
d238419436 | Overparametrized Deep Neural Networks (DNNs) often achieve astounding performances, but may potentially result in severe generalization error. Recently, the relation between the sharpness of the loss landscape and the generalization error has been established byForet et al. (2020), in which the Sharpness Aware Minimize... | EFFICIENT SHARPNESS-AWARE MINIMIZATION FOR IMPROVED TRAINING OF NEURAL NETWORKS |
d263671565 | We study the problem of training a flow-based generative model, parametrized by a two-layer autoencoder, to sample from a high-dimensional Gaussian mixture.We provide a sharp end-to-end analysis of the problem.First, we provide a tight closed-form characterization of the learnt velocity field, when parametrized by a sh... | Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity |
d88522409 | Actor-critic methods solve reinforcement learning problems by updating a parameterized policy known as an actor in a direction that increases an estimate of the expected return known as a critic. However, existing actor-critic methods only use values or gradients of the critic to update the policy parameter. In this pa... | GUIDE ACTOR-CRITIC FOR CONTINUOUS CONTROL |
d249097598 | It is well-known that for sparse linear bandits, when ignoring the dependency on sparsity which is much smaller than the ambient dimension, the worst-case minimax regret is Θ √ dT where d is the ambient dimension and T is the number of rounds. On the other hand, in the benign setting where there is no noise and the act... | VARIANCE-AWARE SPARSE LINEAR BANDITS |
d235422194 | We introduce the maximum n-times coverage problem that selects k overlays to maximize the summed coverage of weighted elements, where each element must be covered at least n times. We also define the min-cost n-times coverage problem where the objective is to select the minimum set of overlays such that the sum of the ... | MAXIMUM n-TIMES COVERAGE FOR VACCINE DESIGN |
d248239638 | Missingness, or the absence of features from an input, is a concept fundamental to many model debugging tools. However, in computer vision, pixels cannot simply be removed from an image. One thus tends to resort to heuristics such as blacking out pixels, which may in turn introduce bias into the debugging process. We s... | MISSINGNESS BIAS IN MODEL DEBUGGING |
d53749372 | We present a new approach to assessing the robustness of neural networks based on estimating the proportion of inputs for which a property is violated. Specifically, we estimate the probability of the event that the property is violated under an input model. Our approach critically varies from the formal verification f... | A STATISTICAL APPROACH TO ASSESSING NEURAL NETWORK ROBUSTNESS |
d13239389 | Deep neural network models, though very powerful and highly successful, are computationally expensive in terms of space and time. Recently, there have been a number of attempts on binarizing the network weights and activations. This greatly reduces the network size, and replaces the underlying multiplications to additi... | LOSS-AWARE BINARIZATION OF DEEP NETWORKS |
d254044324 | The recently developed discrete diffusion models perform extraordinarily well in the text-to-image task, showing significant promise for handling the multimodality signals. In this work, we harness these traits and present a unified multimodal generation model that can conduct both the "modality translation" and "multi... | UNIFIED DISCRETE DIFFUSION FOR SIMULTANEOUS VISION-LANGUAGE GENERATION |
d235624202 | State-of-the-art models in natural language processing rely on separate rigid subword tokenization algorithms, which limit their generalization ability and adaptation to new settings. In this paper, we propose a new model inductive bias that learns a subword tokenization end-to-end as part of the model. To this end, we... | CHARFORMER: FAST CHARACTER TRANSFORMERS VIA GRADIENT-BASED SUBWORD TOKENIZATION |
d247593852 | To train machine learning models that are robust to distribution shifts in the data, distributionally robust optimization (DRO) has been proven very effective. However, the existing approaches to learning a distributionally robust model either require solving complex optimization problems such as semidefinite programmi... | Learning Distributionally Robust Models at Scale via Composite Optimization |
d261696697 | We introduce MAmmoTH, a series of open-source large language models (LLMs) specifically tailored for general math problem-solving.The MAmmoTH models are trained on MathInstruct, our meticulously curated instruction tuning dataset.MathInstruct is compiled from 13 math datasets with intermediate rationales, six of which ... | MAMMOTH: BUILDING MATH GENERALIST MODELS THROUGH HYBRID INSTRUCTION TUNING |
d67856101 | Adaptive optimization methods such as ADAGRAD, RMSPROP and ADAM have been proposed to achieve a rapid training process with an element-wise scaling term on learning rates. Though prevailing, they are observed to generalize poorly compared with SGD or even fail to converge due to unstable and extreme learning rates. Rec... | ADAPTIVE GRADIENT METHODS WITH DYNAMIC BOUND OF LEARNING RATE |
d250526649 | Language models are defined over a finite set of inputs, which creates a vocabulary bottleneck when we attempt to scale the number of supported languages. Tackling this bottleneck results in a trade-off between what can be represented in the embedding matrix and computational issues in the output layer. This paper intr... | LANGUAGE MODELLING WITH PIXELS |
d52920837 | Pruning large neural networks while maintaining the performance is often highly desirable due to the reduced space and time complexity. In existing methods, pruning is incorporated within an iterative optimization procedure with either heuristically designed pruning schedules or additional hyperparameters, undermining ... | SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY |
d246823110 | This work proposes a minimal computational model for learning structured memories of multiple object classes in an incremental setting. Our approach is based on establishing a closed-loop transcription between the classes and a corresponding set of subspaces, known as a linear discriminative representation, in a lowdim... | INCREMENTAL LEARNING OF STRUCTURED MEMORY VIA CLOSED-LOOP TRANSCRIPTION |
d222163237 | While deep reinforcement learning excels at solving tasks where large amounts of data can be collected through virtually unlimited interaction with the environment, learning from limited interaction remains a key challenge. We posit that an agent can learn more efficiently if we augment reward maximization with self-su... | DATA-EFFICIENT REINFORCEMENT LEARNING WITH SELF-PREDICTIVE REPRESENTATIONS |
d254275510 | Many real-world continuous control problems are in the dilemma of weighing the pros and cons, multi-objective reinforcement learning (MORL) serves as a generic framework of learning control policies for different preferences over objectives. However, the existing MORL methods either rely on multiple passes of explicit ... | Q-PENSIEVE: BOOSTING SAMPLE EFFICIENCY OF MULTI-OBJECTIVE RL THROUGH MEMORY SHARING OF Q-SNAPSHOTS |
d52894354 | We identify a class of over-parameterized deep neural networks with standard activation functions and cross-entropy loss which provably have no bad local valley, in the sense that from any point in parameter space there exists a continuous path on which the cross-entropy loss is non-increasing and gets arbitrarily clos... | ON THE LOSS LANDSCAPE OF A CLASS OF DEEP NEURAL NETWORKS WITH NO BAD LOCAL VALLEYS |
d235742951 | One practical challenge in reinforcement learning (RL) is how to make quick adaptations when faced with new environments. In this paper, we propose a principled framework for adaptive RL, called AdaRL, that adapts reliably and efficiently to changes across domains with a few samples from the target domain, even in part... | ADARL: WHAT, WHERE, AND HOW TO ADAPT IN TRANSFER REINFORCEMENT LEARNING |
d947451 | We propose a new algorithm for training generative adversarial networks to jointly learn latent codes for both identities (e.g. individual humans) and observations (e.g. specific photographs). In practice, this means that by fixing the identity portion of latent codes, we can generate diverse images of the same subject... | Semantically Decomposing the Latent Spaces of Generative Adversarial Networks |
d249240346 | Many important problems involving molecular property prediction from 3D structures have limited data, posing a generalization challenge for neural networks. In this paper, we describe a pre-training technique based on denoising that achieves a new state-of-the-art in molecular property prediction by utilizing large dat... | PRE-TRAINING VIA DENOISING FOR MOLECULAR PROPERTY PREDICTION |
d220424770 | We aim to bridge the gap between typical human and machine-learning environments by extending the standard framework of few-shot learning to an online, continual setting. In this setting, episodes do not have separate training and testing phases, and instead models are evaluated online while learning novel classes. As ... | Wandering Within a World: Online Contextualized Few-Shot Learning |
d7451980 | We propose an adversarial training procedure for learning a causal implicit generative model for a given causal graph. We show that adversarial training can be used to learn a generative model with true observational and interventional distributions if the generator architecture is consistent with the given causal grap... | CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training |
d253255046 | Offline reinforcement learning (RL), which refers to decision-making from a previouslycollected dataset of interactions, has received significant attention over the past years. Much effort has focused on improving offline RL practicality by addressing the prevalent issue of partial data coverage through various forms o... | Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian |
d264439657 | We report the presence of a simple neural mechanism that represents an inputoutput function as a vector within autoregressive transformer language models (LMs).Using causal mediation analysis on a diverse range of in-context-learning (ICL) tasks, we find that a small number attention heads transport a compact represent... | FUNCTION VECTORS IN LARGE LANGUAGE MODELS |
d38294295 | Towards bridging the gap between machine and human intelligence, it is of utmost importance to introduce environments that are visually realistic and rich in content. In such environments, one can evaluate and improve a crucial property of practical intelligent systems, namely generalization. In this work, we build Hou... | BUILDING GENERALIZABLE AGENTS WITH A REALISTIC AND RICH 3D ENVIRONMENT |
d243832613 | Reinforcement learning can train policies that effectively perform complex tasks. However for long-horizon tasks, the performance of these methods degrades with horizon, often necessitating reasoning over and chaining lower-level skills. Hierarchical reinforcement learning aims to enable this by providing a bank of low... | VALUE FUNCTION SPACES: SKILL-CENTRIC STATE ABSTRACTIONS FOR LONG-HORIZON REASONING |
d209478454 | In many applications, it is desirable to extract only the relevant information from complex input data, which involves making a decision about which input features are relevant. The information bottleneck method formalizes this as an informationtheoretic optimization problem by maintaining an optimal tradeoff between c... | THE VARIATIONAL BANDWIDTH BOTTLENECK: STOCHASTIC EVALUATION ON AN INFORMATION BUDGET |
d210848001 | We propose Additive Powers-of-Two (APoT) quantization, an efficient nonuniform quantization scheme for the bell-shaped and long-tailed distribution of weights and activations in neural networks. By constraining all quantization levels as the sum of Powers-of-Two terms, APoT quantization enjoys high computational effici... | ADDITIVE POWERS-OF-TWO QUANTIZATION: AN EFFICIENT NON-UNIFORM DISCRETIZATION FOR NEURAL NETWORKS |
d237504552 | Evaluating the general abilities of intelligent agents requires complex simulation environments. Existing benchmarks typically evaluate only one narrow task per environment, requiring researchers to perform expensive training runs on many different environments. We introduce Crafter, an open world survival game with vi... | BENCHMARKING THE SPECTRUM OF AGENT CAPABILITIES |
d249191498 | Recently, it has been shown that neural networks not only approximate the groundstate wave functions of a single molecular system well but can also generalize to multiple geometries. While such generalization significantly speeds up training, each energy evaluation still requires Monte Carlo integration which limits th... | SAMPLING-FREE INFERENCE FOR AB-INITIO POTENTIAL ENERGY SURFACE NETWORKS |
d252846717 | In silico prediction of the ligand binding pose to a given protein target is a crucial but challenging task in drug discovery. This work focuses on flexible blind selfdocking, where we aim to predict the positions, orientations and conformations of docked molecules. Traditional physics-based methods usually suffer from... | E3BIND: AN END-TO-END EQUIVARIANT NETWORK FOR PROTEIN-LIGAND DOCKING |
d243985604 | Background. Commonly, Deep Neural Networks (DNNs) generalize well on samples drawn from a distribution similar to that of the training set. However, DNNs' predictions are brittle and unreliable when the test samples are drawn from a dissimilar distribution. This is a major concern for deployment in realworld applicatio... | A STATISTICAL FRAMEWORK FOR EFFICIENT OUT OF DISTRIBUTION DETECTION IN DEEP NEURAL NETWORKS |
d2421899 | showed that pair-wise products of features in a convolutional network are a very effective representation of image textures. We propose a simple modification to that representation which makes it possible to incorporate longrange structure into image generation, and to render images that satisfy various symmetry constr... | Incorporating long-range consistency in CNN-based texture generation |
d29159323 | Understanding how people represent categories is a core problem in cognitive science. Decades of research have yielded a variety of formal theories of categories, but validating them with naturalistic stimuli is difficult. The challenge is that human category representations cannot be directly observed and running info... | Capturing human category representations by sampling in deep feature spaces |
d249538529 | Value approximation using deep neural networks is at the heart of off-policy deep reinforcement learning, and is often the primary module that provides learning signals to the rest of the algorithm. While multi-layer perceptron networks are universal function approximators, recent works in neural kernel regression sugg... | OVERCOMING THE SPECTRAL BIAS OF NEURAL VALUE APPROXIMATION |
d54464191 | Batch Normalization (BN) has become a cornerstone of deep learning across diverse architectures, appearing to help optimization as well as generalization. While the idea makes intuitive sense, theoretical analysis of its effectiveness has been lacking. Here theoretical support is provided for one of its conjectured pro... | THEORETICAL ANALYSIS OF AUTO RATE-TUNING BY BATCH NORMALIZATION |
d60440536 | The posteriors over neural network weights are high dimensional and multimodal. Each mode typically characterizes a meaningfully different representation of the data. We develop Cyclical Stochastic Gradient MCMC (SG-MCMC) to automatically explore such distributions. In particular, we propose a cyclical stepsize schedul... | CYCLICAL STOCHASTIC GRADIENT MCMC FOR BAYESIAN DEEP LEARNING |
d52144552 | Empirical risk minimization (ERM), with proper loss function and regularization, is the common practice of supervised classification. In this paper, we study training arbitrary (from linear to deep) binary classifier from only unlabeled (U) data by ERM. We prove that it is impossible to estimate the risk of an arbitrar... | On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data |
d256697612 | Disentangled learning representations have promising utility in many applications, but they currently suffer from serious reliability issues. We present Gaussian Channel Autoencoder (GCAE), a method which achieves reliable disentanglement via flexible density estimation of the latent space. GCAE avoids the curse of dim... | DISENTANGLING LEARNING REPRESENTATIONS WITH DENSITY ESTIMATION |
d52002336 | In many tasks, in particular in natural science, the goal is to determine hidden system parameters from a set of measurements. Often, the forward process from parameter-to measurement-space is a well-defined function, whereas the inverse problem is ambiguous: one measurement may map to multiple different sets of parame... | ANALYZING INVERSE PROBLEMS WITH INVERTIBLE NEURAL NETWORKS |
d252565121 | No existing spherical convolutional neural network (CNN) framework is both computationally scalable and rotationally equivariant. Continuous approaches capture rotational equivariance but are often prohibitively computationally demanding. Discrete approaches offer more favorable computational performance but at the cos... | SCALABLE AND EQUIVARIANT SPHERICAL CNNS BY DISCRETE-CONTINUOUS (DISCO) CONVOLUTIONS |
d259837388 | We investigate the mechanism design problem faced by a principal who hires multiple agents to gather and report costly information. Then, the principal exploits the information to make an informed decision. We model this problem as a game, where the principal announces a mechanism consisting in action recommendations a... | Online Information Acquisition: Hiring Multiple Agents |
d258212748 | A Neural Process (NP) estimates a stochastic process implicitly defined with neural networks given a stream of data, rather than pre-specifying priors already known, such as Gaussian processes. An ideal NP would learn everything from data without any inductive biases, but in practice, we often restrict the class of sto... | MARTINGALE POSTERIOR NEURAL PROCESSES |
d32432826 | With the increasing popularity of non-convex deep models, developing a unifying theory for studying the optimization problems that arise from training these models becomes very significant. Toward this end, we present in this paper a unifying landscape analysis framework that can be used when the training objective fun... | Learning Deep Models: Critical Points and Local Openness |
d259138458 | In contrast to classical reinforcement learning, distributional RL algorithms aim to learn the distribution of returns rather than their expected value. Since the nature of the return distribution is generally unknown a priori or arbitrarily complex, a common approach finds approximations within a set of representable,... | Diverse Projection Ensembles for Distributional Reinforcement Learning |
d251929045 | As machine learning (ML) models are increasingly being deployed in high-stakes applications, policymakers have suggested tighter data protection regulations (e.g., GDPR, CCPA).One key principle is the "right to be forgotten" which gives users the right to have their data deleted.Another key principle is the right to an... | ON THE TRADE-OFF BETWEEN ACTIONABLE EXPLANATIONS AND THE RIGHT TO BE FORGOTTEN |
d238408063 | Transformer-based models have achieved great success in various NLP, vision, and speech tasks. However, the core of Transformer, the self-attention mechanism, has a quadratic time and memory complexity with respect to the sequence length, which hinders applications of Transformer-based models to long sequences. Many ap... | PONET: POOLING NETWORK FOR EFFICIENT TOKEN MIXING IN LONG SEQUENCES |
d232092291 | Deep learning has seen a movement away from representing examples with a monolithic hidden state towards a richly structured state. For example, Transformers segment by position, and object-centric architectures decompose images into entities. In all these architectures, interactions between different elements are mode... | COORDINATION AMONG NEURAL MODULES THROUGH A SHARED GLOBAL WORKSPACE |
d246679851 | Solving goal-conditioned tasks with sparse rewards using self-supervised learning is promising because of its simplicity and stability over current reinforcement learning (RL) algorithms. A recent work, called Goal-Conditioned Supervised Learning (GCSL), provides a new learning framework by iteratively relabeling and i... | RETHINKING GOAL-CONDITIONED SUPERVISED LEARNING AND ITS CONNECTION TO OFFLINE RL |
d231861903 | Training Reinforcement Learning (RL) agents online in high-stakes applications is often prohibitive due to the risk associated with exploration. Thus, the agent can only use data previously collected by safe policies. While previous work considers optimizing the average performance using offline data, we focus on optim... | RISK-AVERSE OFFLINE REINFORCEMENT LEARNING |
d247447402 | Learning to reason about relations and dynamics over multiple interacting objects is a challenging topic in machine learning. The challenges mainly stem from that the interacting systems are exponentially-compositional, symmetrical, and commonly geometrically-constrained. Current methods, particularly the ones based on... | EQUIVARIANT GRAPH MECHANICS NETWORKS WITH CONSTRAINTS |
d231854181 | In the segmentation of fine-scale structures from natural and biomedical images, per-pixel accuracy is not the only metric of concern. Topological correctness, such as vessel connectivity and membrane closure, is crucial for downstream analysis tasks. In this paper, we propose a new approach to train deep image segment... | TOPOLOGY-AWARE SEGMENTATION USING DISCRETE MORSE THEORY |
d254044221 | We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. It is based on two key components: (i) segmentation of time series into subseries-level patches which are served as input tokens to Transformer; (ii) channel-independence wher... | A TIME SERIES IS WORTH 64 WORDS: LONG-TERM FORECASTING WITH TRANSFORMERS |
d211020709 | Strictly enforcing orthonormality constraints on parameter matrices has been shown advantageous in deep learning. This amounts to Riemannian optimization on the Stiefel manifold, which, however, is computationally expensive. To address this challenge, we present two main contributions: (1) A new efficient retraction ma... | EFFICIENT RIEMANNIAN OPTIMIZATION ON THE STIEFEL MANIFOLD VIA THE CAYLEY TRANSFORM |
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