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d259164791 | The bandits with knapsack (BwK) framework models online decision-making problems in which an agent makes a sequence of decisions subject to resource consumption constraints. The traditional model assumes that each action consumes a non-negative amount of resources and the process ends when the initial budgets are fully... | Bandits with Replenishable Knapsacks: the Best of both Worlds |
d248392450 | We explore a new perspective on video understanding by casting the video recognition problem as an image recognition task. Our approach rearranges input video frames into super images, which allow for training an image classifier directly to fulfill the task of action recognition, in exactly the same way as image class... | CAN AN IMAGE CLASSIFIER SUFFICE FOR ACTION RECOGNITION? |
d252993084 | Image copy detection and retrieval from large databases leverage two components. First, a neural network maps an image to a vector representation, that is relatively robust to various transformations of the image. Second, an efficient but approximate similarity search algorithm trades scalability (size and speed) again... | Active Image Indexing |
d220968978 | The transformer and BERT models pushed the performance on NLP tasks to new levels via their attention mechanism. We show that this attention mechanism is the update rule of a modern Hopfield network with continuous states. This new Hopfield network can store exponentially (with the dimension) many patterns, converges w... | Hopfield Networks is All You Need |
d49671490 | In this paper, we propose a novel Convolutional Neural Network (CNN) architecture for learning multi-scale feature representations with good tradeoffs between speed and accuracy. This is achieved by using a multi-branch network, which has different computational complexity at different branches. Through frequent mergin... | Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition |
d33897266 | Tensor train (TT) decomposition is a powerful representation for high-order tensors, which has been successfully applied to various machine learning tasks in recent years. However, since the tensor product is not commutative, permutation of data dimensions makes solutions and TT-ranks of TT decomposition inconsistent. ... | Learning Efficient Tensor Representations with Ring Structure Networks |
d199543783 | This paper introduces the Behaviour Suite for Reinforcement Learning, or bsuite for short. bsuite is a collection of carefully-designed experiments that investigate core capabilities of reinforcement learning (RL) agents with two objectives. First, to collect clear, informative and scalable problems that capture key is... | Behaviour Suite for Reinforcement Learning |
d221879065 | This paper aims to understand and improve the utility of the dropout operation from the perspective of game-theoretic interactions. We prove that dropout can suppress the strength of interactions between input variables of deep neural networks (DNNs). The theoretical proof is also verified by various experiments. Furth... | INTERPRETING AND BOOSTING DROPOUT FROM A GAME-THEORETIC VIEW |
d202538896 | This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial conditions. We also introduce a suite of eight tasks that combine these three properties, and show that R2D3 can solve several of the tasks... | Making Efficient Use of Demonstrations to Solve Hard Exploration Problems |
d258170309 | Masked autoencoders (MAE) have recently been introduced to 3D self-supervised pretraining for point clouds due to their great success in NLP and computer vision. Unlike MAEs used in the image domain, where the pretext task is to restore features at the masked pixels, such as colors, the existing 3D MAE works reconstruc... | 3D Feature Prediction for Masked-AutoEncoder-Based Point Cloud Pretraining |
d246430476 | Differing from the well-developed horizontal object detection area whereby the computing-friendly IoU based loss is readily adopted and well fits with the detection metrics. In contrast, rotation detectors often involve a more complicated loss based on SkewIoU which is unfriendly to gradient-based training. In this pap... | THE KFIOU LOSS FOR ROTATED OBJECT DETECTION |
d27174168 | We propose a framework for adversarial training that relies on a sample rather than a single sample point as the fundamental unit of discrimination. Inspired by discrepancy measures and twosample tests between probability distributions, we propose two such distributional adversaries that operate and predict on samples,... | Distributional Adversarial Networks |
d58981508 | Layout is important for graphic design and scene generation. We propose a novel Generative Adversarial Network, called LayoutGAN, that synthesizes layouts by modeling geometric relations of different types of 2D elements. The generator of LayoutGAN takes as input a set of randomly-placed 2D graphic elements and uses se... | LAYOUTGAN: GENERATING GRAPHIC LAYOUTS WITH WIREFRAME DISCRIMINATORS |
d203591628 | Simultaneous machine translation models start generating a target sequence before they have encoded or read the source sequence. Recent approaches for this task either apply a fixed policy on a state-of-the art Transformer model, or a learnable monotonic attention on a weaker recurrent neural network-based structure. I... | MONOTONIC MULTIHEAD ATTENTION |
d264172174 | Large Language Models like ChatGPT demonstrate a remarkable capacity to learn new concepts during inference without any fine-tuning. However, visual models trained to detect new objects during inference have been unable to replicate this ability, and instead either perform poorly or require meta-training and/or finetun... | CONTEXT-AWARE META-LEARNING |
d252815807 | We study embodied reference understanding, the task of locating referents using embodied gestural signals and language references. Human studies have revealed that objects referred to or pointed to do not lie on the elbow-wrist line, a common misconception; instead, they lie on the so-called virtual touch line. However... | UNDERSTANDING EMBODIED REFERENCE WITH TOUCH-LINE TRANSFORMER |
d27254961 | Deep reinforcement learning algorithms can learn complex behavioral skills, but real-world application of these methods requires a large amount of experience to be collected by the agent. In practical settings, such as robotics, this involves repeatedly attempting a task, resetting the environment between each attempt.... | Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning |
d207869899 | Quantum computing is a new computational paradigm that promises applications in several fields, including machine learning. In the last decade, deep learning, and in particular Convolutional neural networks (CNN), have become essential for applications in signal processing and image recognition[1,2]. Quantum deep learn... | Quantum Algorithms for Deep Convolutional Neural Networks |
d238856856 | Equivariance has emerged as a desirable property of representations of objects subject to identity-preserving transformations that constitute a group, such as translations and rotations. However, the expressivity of a representation constrained by group equivariance is still not fully understood. We address this gap by... | CAPACITY OF GROUP-INVARIANT LINEAR READOUTS FROM EQUIVARIANT REPRESENTATIONS: HOW MANY OBJECTS CAN BE LINEARLY CLASSIFIED UNDER ALL POSSIBLE VIEWS? |
d58981389 | Stability is a fundamental property of dynamical systems, yet to this date it has had little bearing on the practice of recurrent neural networks. In this work, we conduct a thorough investigation of stable recurrent models. Theoretically, we prove stable recurrent neural networks are well approximated by feed-forward ... | Stable Recurrent Models |
d235683534 | Experiments with pre-trained models such as BERT are often based on a single checkpoint. While the conclusions drawn apply to the artifact tested in the experiment (i.e., the particular instance of the model), it is not always clear whether they hold for the more general procedure which includes the architecture, train... | THE MULTIBERTS: BERT REPRODUCTIONS FOR ROBUSTNESS ANALYSIS |
d238419305 | Deep neural networks (DNNs) often rely on easy-to-learn discriminatory features, or cues, that are not necessarily essential to the problem at hand. For example, ducks in an image may be recognized based on their typical background scenery, such as lakes or streams. This phenomenon, also known as shortcut learning, is ... | WHICH SHORTCUT CUES WILL DNNS CHOOSE? A STUDY FROM THE PARAMETER-SPACE PERSPECTIVE |
d253244237 | Research in mechanistic interpretability seeks to explain behaviors of machine learning (ML) models in terms of their internal components. However, most previous work either focuses on simple behaviors in small models or describes complicated behaviors in larger models with broad strokes. In this work, we bridge this g... | INTERPRETABILITY IN THE WILD: A CIRCUIT FOR INDIRECT OBJECT IDENTIFICATION IN GPT-2 SMALL |
d220347682 | The goal of domain generalization algorithms is to predict well on distributions different from those seen during training. While a myriad of domain generalization algorithms exist, inconsistencies in experimental conditions-datasets, architectures, and model selection criteria-render fair and realistic comparisons dif... | In Search of Lost Domain Generalization |
d252111177 | We present rectified flow, a surprisingly simple approach to learning (neural) ordinary differential equation (ODE) models to transport between two empirically observed distributions π 0 and π 1 , hence providing a unified solution to generative modeling and domain transfer, among various other tasks involving distribu... | Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow |
d12308095 | This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system. Specifically, we study applying image transformations such as bit-depth reduction, JPEG compression, total variance minimization, and image qui... | COUNTERING ADVERSARIAL IMAGES USING INPUT TRANSFORMATIONS |
d248811555 | This paper studies the cooperative learning of two generative flow models, in which the two models are iteratively updated based on the jointly synthesized examples. The first flow model is a normalizing flow that transforms an initial simple density into a target density by applying a sequence of invertible transforma... | A TALE OF TWO FLOWS: COOPERATIVE LEARNING OF LANGEVIN FLOW AND NORMALIZING FLOW TOWARD ENERGY-BASED MODEL |
d56895471 | We study two types of preconditioners and preconditioned stochastic gradient descent (SGD) methods in a unified framework. We call the first one the Newton type due to its close relationship to the Newton method, and the second one the Fisher type as its preconditioner is closely related to the inverse of Fisher inform... | PRECONDITIONER ON MATRIX LIE GROUP FOR SGD |
d259224562 | Reinforcement learning often needs to deal with the exponential growth of states and actions when exploring optimal control in high-dimensional spaces (often known as the curse of dimensionality). In this work, we address this issue by learning the inherent structure of action-wise similar MDP to appropriately balance ... | Achieving Sample and Computational Efficient Reinforcement Learning by Action Space Reduction via Grouping |
d247084286 | Recently, brain-inspired spiking neuron networks (SNNs) have attracted widespread research interest because of their event-driven and energy-efficient characteristics. Still, it is difficult to efficiently train deep SNNs due to the nondifferentiability of its activation function, which disables the typically used grad... | TEMPORAL EFFICIENT TRAINING OF SPIKING NEURAL NETWORK VIA GRADIENT RE-WEIGHTING |
d257353502 | Despite their remarkable achievement, gigantic transformers encounter significant drawbacks, including exorbitant computational and memory footprints during training, as well as severe collapse evidenced by a high degree of parameter redundancy. Sparsely-activated Mixture-of-Experts (SMoEs) have shown promise to mitiga... | SPARSE MOE AS THE NEW DROPOUT: SCALING DENSE AND SELF-SLIMMABLE TRANSFORMERS |
d53215110 | Neural machine translation (NMT) models learn representations containing substantial linguistic information. However, it is not clear if such information is fully distributed or if some of it can be attributed to individual neurons. We develop unsupervised methods for discovering important neurons in NMT models. Our me... | IDENTIFYING AND CONTROLLING IMPORTANT NEURONS IN NEURAL MACHINE TRANSLATION |
d211082491 | Magnitude-based pruning is one of the simplest methods for pruning neural networks. Despite its simplicity, magnitude-based pruning and its variants demonstrated remarkable performances for pruning modern architectures. Based on the observation that magnitude-based pruning indeed minimizes the Frobenius distortion of a... | LOOKAHEAD: A FAR-SIGHTED ALTERNATIVE OF MAGNITUDE-BASED PRUNING |
d57373762 | Stochastic gradient descent (SGD) has been found to be surprisingly effective in training a variety of deep neural networks. However, there is still a lack of understanding on how and why SGD can train these complex networks towards a global minimum. In this study, we establish the convergence of SGD to a global minimu... | SGD CONVERGES TO GLOBAL MINIMUM IN DEEP LEARNING VIA STAR-CONVEX PATH |
d13570924 | This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks. The framework can successfully train both deep discriminative models and deep generative models in compl... | Variational Continual Learning |
d60440651 | Generative Adversarial Networks (GANs) are one of the most popular tools for learning complex high dimensional distributions. However, generalization properties of GANs have not been well understood. In this paper, we analyze the generalization of GANs in practical settings. We show that discriminators trained on discr... | IMPROVING GENERALIZATION AND STABILITY OF GENERATIVE ADVERSARIAL NETWORKS |
d53073405 | Multiview stereo aims to reconstruct scene depth from images acquired by a camera under arbitrary motion. Recent methods address this problem through deep learning, which can utilize semantic cues to deal with challenges such as textureless and reflective regions. In this paper, we present a convolutional neural networ... | DPSNET: END-TO-END DEEP PLANE SWEEP STEREO |
d3624410 | We analyze the convergence of (stochastic) gradient descent algorithm for learning a convolutional filter with Rectified Linear Unit (ReLU) activation function. Our analysis does not rely on any specific form of the input distribution and our proofs only use the definition of ReLU, in contrast with previous works that ... | When is a Convolutional Filter Easy to Learn? |
d253018703 | Diffusion models achieve outstanding generative performance in various domains. Despite their great success, they lack semantic latent space which is essential for controlling the generative process. To address the problem, we propose asymmetric reverse process (Asyrp) which discovers the semantic latent space in froze... | DIFFUSION MODELS ALREADY HAVE A SEMANTIC LATENT SPACE |
d247218521 | Graph Neural Networks (GNNs) have achieved great success in various tasks, but their performance highly relies on a large number of labeled nodes, which typically requires considerable human effort. GNN-based Active Learning (AL) methods are proposed to improve the labeling efficiency by selecting the most valuable nod... | INFORMATION GAIN PROPAGATION: A NEW WAY TO GRAPH ACTIVE LEARNING WITH SOFT LABELS |
d224705241 | Recent advances in semi-supervised learning (SSL) demonstrate that a combination of consistency regularization and pseudo-labeling can effectively improve image classification accuracy in the low-data regime. Compared to classification, semantic segmentation tasks require much more intensive labeling costs. Thus, these... | PSEUDOSEG: DESIGNING PSEUDO LABELS FOR SEMANTIC SEGMENTATION |
d52182515 | We explore the concept of co-design in the context of neural network verification. Specifically, we aim to train deep neural networks that not only are robust to adversarial perturbations but also whose robustness can be verified more easily. To this end, we identify two properties of network models -weight sparsity an... | TRAINING FOR FASTER ADVERSARIAL ROBUSTNESS VERIFICATION VIA INDUCING RELU STABILITY |
d259165244 | The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations. Rather than merely exploring the breadth of LLM abilities, we believe meticulous and thoughtful designs are essential to thorough, unbiased, and applicable evaluations. Given the importance of world knowledge to LLMs... | KoLA: Carefully Benchmarking World Knowledge of Large Language Models |
d256104949 | Reconstructing 3D point clouds into triangle meshes is a key problem in computational geometry and surface reconstruction. Point cloud triangulation solves this problem by providing edge information to the input points. Since no vertex interpolation is involved, it is beneficial to preserve sharp details on the surface... | CIRCNET: MESHING 3D POINT CLOUDS WITH CIRCUMCENTER DETECTION |
d258887582 | Latent Graph Inference (LGI) relaxed the reliance of Graph Neural Networks (GNNs) on a given graph topology by dynamically learning it. However, most of LGI methods assume to have a (noisy, incomplete, improvable, ...) input graph to rewire and can solely learn regular graph topologies. In the wake of the success of To... | From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module |
d219721312 | We assess the tendency of state-of-the-art object recognition models to depend on signals from image backgrounds. We create a toolkit for disentangling foreground and background signal on ImageNet images, and find that (a) models can achieve non-trivial accuracy by relying on the background alone, (b) models often misc... | Noise or Signal: The Role of Image Backgrounds in Object Recognition |
d2514328 | Recurrent neural networks (RNNs) have been used extensively and with increasing success to model various types of sequential data. Much of this progress has been achieved through devising recurrent units and architectures with the flexibility to capture complex statistics in the data, such as long range dependency or l... | VARIABLE COMPUTATION IN RECURRENT NEURAL NETWORKS |
d252815905 | No-press Diplomacy is a complex strategy game involving both cooperation and competition that has served as a benchmark for multi-agent AI research. While self-play reinforcement learning has resulted in numerous successes in purely adversarial games like chess, Go, and poker, self-play alone is insufficient for achiev... | MASTERING THE GAME OF NO-PRESS DIPLOMACY VIA HUMAN-REGULARIZED REINFORCEMENT LEARNING AND PLANNING |
d247519082 | Recent advances in self-supervised learning have experienced remarkable progress, especially for contrastive learning based methods, which regard each image as well as its augmentations as an individual class and try to distinguish them from all other images. However, due to the large quantity of exemplars, this kind o... | BAG OF INSTANCES AGGREGATION BOOSTS SELF-SUPERVISED DISTILLATION |
d261682404 | Training energy-based models (EBMs) with maximum likelihood estimation on high-dimensional data can be both challenging and time-consuming. As a result, there a noticeable gap in sample quality between EBMs and other generative frameworks like GANs and diffusion models. To close this gap, inspired by the recent efforts... | Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood |
d219792740 | Gradient boosting is a powerful machine learning technique that is particularly successful for tasks containing heterogeneous features and noisy data. While gradient boosting classification models return a distribution over class labels, regressions models typically yield only point predictions. However, for many pract... | Uncertainty in Gradient Boosting via Ensembles |
d259861536 | Data-free model stealing aims to replicate a target model without direct access to either the training data or the target model.To accomplish this, existing methods use a generator to produce samples in order to train a student model to match the target model outputs.To this end, the two main challenges are estimating ... | DUAL STUDENT NETWORKS FOR DATA-FREE MODEL STEALING |
d246285537 | 3D object detection is a fundamental and challenging task for 3D scene understanding, and the monocular-based methods can serve as an economical alternative to the stereo-based or LiDAR-based methods. However, accurately detecting objects in the 3D space from a single image is extremely difficult due to the lack of spa... | MONODISTILL: LEARNING SPATIAL FEATURES FOR MONOCULAR 3D OBJECT DETECTION |
d2468625 | There are families of neural networks that can learn to compute any function, provided sufficient training data. However, given that in practice training data is scarce for all but a small set of problems, a core question is how to incorporate prior knowledge into a model. Here we consider the case of prior procedural ... | Programming with a Differentiable Forth Interpreter |
d237353222 | Large-scale pre-trained language models have contributed significantly to natural language processing by demonstrating remarkable abilities as few-shot learners. However, their effectiveness depends mainly on scaling the model parameters and prompt design, hindering their implementation in most real-world applications.... | DIFFERENTIABLE PROMPT MAKES PRE-TRAINED LANGUAGE MODELS BETTER FEW-SHOT LEARNERS |
d17984798 | Recent papers have shown that neural networks obtain state-of-the-art performance on several different sequence tagging tasks. One appealing property of such systems is their generality, as excellent performance can be achieved with a unified architecture and without task-specific feature engineering. However, it is un... | TRANSFER LEARNING FOR SEQUENCE TAGGING WITH HIERARCHICAL RECURRENT NETWORKS |
d238408445 | Many gradient-based meta-learning methods assume a set of parameters that do not participate in inner-optimization, which can be considered as hyperparameters. Although such hyperparameters can be optimized using the existing gradientbased hyperparameter optimization (HO) methods, they suffer from the following issues.... | ONLINE HYPERPARAMETER META-LEARNING WITH HYPERGRADIENT DISTILLATION |
d253581330 | SGD and AdamW are the two most used optimizers for fine-tuning large neural networks in computer vision.When the two methods perform the same, SGD is preferable because it uses less memory (12 bytes/parameter with momentum and 8 bytes/parameter without) than AdamW (16 bytes/parameter).However, on a suite of downstream ... | How to Fine-Tune Vision Models with SGD |
d263889455 | TE VISUAL TOKENIZATION
29 Sep 20234C8D833F4622C6A583127C3A667E25A5arXiv:2309.04669v2[cs.CV]
Recently, the remarkable advance of the Large Language Model (LLM) has inspired researchers to transfer its extraordinary reasoning capability to both vision and language data.However, the prevailing approaches primarily regard ... | UNIFIED LANGUAGE-VISION PRETRAINING IN LLM WITH DYNAMIC DISCRETE VISUAL TOKENIZATION |
d247222973 | We consider a generic min-max multi-objective bilevel optimization problem with applications in robust machine learning such as representation learning and hyperparameter optimization. We design MORBiT, a novel single-loop gradient descent-ascent bilevel optimization algorithm, to solve the generic problem and present ... | Min-Max Multi-objective Bilevel Optimization with Applications in Robust Machine Learning |
d108334676 | In this article we use rate-distortion theory, a branch of information theory devoted to the problem of lossy compression, to shed light on an important problem in latent variable modeling of data: is there room to improve the model? One way to address this question is to find an upper bound on the probability (equival... | INFORMATION THEORETIC LOWER BOUNDS ON NEGATIVE LOG LIKELIHOOD |
d245131359 | Score-based generative models (SGMs) have demonstrated remarkable synthesis quality. SGMs rely on a diffusion process that gradually perturbs the data towards a tractable distribution, while the generative model learns to denoise. The complexity of this denoising task is, apart from the data distribution itself, unique... | SCORE-BASED GENERATIVE MODELING WITH CRITICALLY-DAMPED LANGEVIN DIFFUSION |
d263609258 | Normalization layers are one of the key building blocks for deep neural networks.Several theoretical studies have shown that batch normalization improves the signal propagation, by avoiding the representations from becoming collinear across the layers.However, results on mean-field theory of batch normalization also co... | Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion |
d238531510 | Multi-agent reinforcement learning has made substantial empirical progresses in solving games with a large number of players. However, theoretically, the best known sample complexity for finding a Nash equilibrium in general-sum games scales exponentially in the number of players due to the size of the joint action spa... | When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently? |
d263671852 | Posterior sampling has been shown to be a powerful Bayesian approach for solving imaging inverse problems.The recent plug-and-play unadjusted Langevin algorithm (PnP-ULA) has emerged as a promising method for Monte Carlo sampling and minimum mean squared error (MMSE) estimation by combining physical measurement models ... | Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Models |
d55481903 | Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single classification tasks. Our goal is to understand whether a related phenomenon occurs when data does not undergo a clear distributional shift. We define a "forgetting event" to have occur... | AN EMPIRICAL STUDY OF EXAMPLE FORGETTING DURING DEEP NEURAL NETWORK LEARNING |
d222179041 | Mesh-based simulations are central to modeling complex physical systems in many disciplines across science and engineering. Mesh representations support powerful numerical integration methods and their resolution can be adapted to strike favorable trade-offs between accuracy and efficiency. However, highdimensional sci... | LEARNING MESH-BASED SIMULATION WITH GRAPH NETWORKS |
d3515208 | Stochastic gradient descent (SGD) is widely believed to perform implicit regularization when used to train deep neural networks, but the precise manner in which this occurs has thus far been elusive. We prove that SGD minimizes an average potential over the posterior distribution of weights along with an entropic regul... | STOCHASTIC GRADIENT DESCENT PERFORMS VARIATIONAL INFERENCE, CONVERGES TO LIMIT CYCLES FOR DEEP NETWORKS |
d3687439 | We present a method for transferring neural representations from label-rich source domains to unlabeled target domains. Recent adversarial methods proposed for this task learn to align features across domains by fooling a special domain critic network. However, a drawback of this approach is that the critic simply labe... | ADVERSARIAL DROPOUT REGULARIZATION |
d211678252 | Batch normalization (BatchNorm) has become an indispensable tool for training deep neural networks, yet it is still poorly understood. Although previous work has typically focused on its normalization component, BatchNorm also adds two per-feature trainable parameters: a coefficient and a bias. However, the role and ex... | Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs |
d238583203 | This paper introduces Multi-Agent MDP Homomorphic Networks, a class of networks that allows distributed execution using only local information, yet is able to share experience between global symmetries in the joint state-action space of cooperative multi-agent systems. In cooperative multi-agent systems, complex symmet... | MULTI-AGENT MDP HOMOMORPHIC NETWORKS |
d264439509 | We study the ability of state-of-the art models to answer constraint satisfaction queries for information retrieval (e.g., "a list of ice cream shops in San Diego").In the past, such queries were considered to be tasks that could only be solved via web-search or knowledge bases.More recently, large language models (LLM... | KITAB: EVALUATING LLMS ON CONSTRAINT SATISFACTION FOR INFORMATION RETRIEVAL |
d221516475 | We propose a new test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. We find that while most recent mode... | Measuring Massive Multitask Language Understanding |
d211252411 | This paper studies few-shot learning via representation learning, where one uses T source tasks with n 1 data per task to learn a representation in order to reduce the sample complexity of a target task for which there is only n 2 (≪ n 1 ) data. Specifically, we focus on the setting where there exists a good common rep... | Few-Shot Learning via Learning the Representation, Provably |
d213704197 | Modern neural network architectures use structured linear transformations, such as low-rank matrices, sparse matrices, permutations, and the Fourier transform, to improve inference speed and reduce memory usage compared to general linear maps. However, choosing which of the myriad structured transformations to use (and... | Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps |
d222208577 | Multi-objective optimization problems are prevalent in machine learning. These problems have a set of optimal solutions, called the Pareto front, where each point on the front represents a different trade-off between possibly conflicting objectives. Recent optimization algorithms can target a specific desired ray in lo... | Learning the Pareto Front with Hypernetworks |
d3458858 | In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification. However, they were shown to be vulnerable to adversarial perturbations: carefully crafted small perturbations can cause misclassification of legitimate images. We propose Defense-GAN, a new fra... | DEFENSE-GAN: PROTECTING CLASSIFIERS AGAINST ADVERSARIAL ATTACKS USING GENERATIVE MODELS |
d257019874 | This paper introduces a family of stochastic extragradient-type algorithms for a class of nonconvex-nonconcave problems characterized by the weak Minty variational inequality (MVI). Unlike existing results on extragradient methods in the monotone setting, employing diminishing stepsizes is no longer possible in the wea... | Solving stochastic weak Minty variational inequalities without increasing batch size |
d247628243 | A fundamental characteristic of natural language is the high rate at which speakers produce novel expressions. Because of this novelty, a heavy-tail of rare events accounts for a significant amount of the total probability mass of distributions in language(Baayen, 2001). Standard language modeling metrics such as perpl... | EVALUATING DISTRIBUTIONAL DISTORTION IN NEURAL LANGUAGE MODELING |
d232257793 | We propose HyperDynamics, a dynamics meta-learning framework that conditions on an agent's interactions with the environment and optionally its visual observations, and generates the parameters of neural dynamics models based on inferred properties of the dynamical system. Physical and visual properties of the environm... | HYPERDYNAMICS: META-LEARNING OBJECT AND AGENT DYNAMICS WITH HYPERNETWORKS |
d222290886 | Invariant Causal Prediction(Peters et al., 2016)is a technique for out-of-distribution generalization which assumes that some aspects of the data distribution vary across the training set but that the underlying causal mechanisms remain constant. Recently, Arjovsky et al.(2019)proposed Invariant Risk Minimization (IRM)... | The Risks of Invariant Risk Minimization |
d219981134 | When a missing process depends on the missing values themselves, it needs to be explicitly modelled and taken into account while doing likelihood-based inference. We present an approach for building and fitting deep latent variable models (DLVMs) in cases where the missing process is dependent on the missing data. Spec... | not-MIWAE: Deep Generative Modelling with Missing not at Random Data |
d231693079 | Trust region methods are a popular tool in reinforcement learning as they yield robust policy updates in continuous and discrete action spaces. However, enforcing such trust regions in deep reinforcement learning is difficult. Hence, many approaches, such as Trust Region Policy Optimization (TRPO) and Proximal Policy O... | DIFFERENTIABLE TRUST REGION LAYERS FOR DEEP REINFORCEMENT LEARNING |
d52941845 | Binarized Neural Networks (BNNs) have recently attracted significant interest due to their computational efficiency. Concurrently, it has been shown that neural networks may be overly sensitive to "attacks" -tiny adversarial changes in the input -which may be detrimental to their use in safety-critical domains. Designi... | COMBINATORIAL ATTACKS ON BINARIZED NEURAL NETWORKS |
d249848272 | We propose UNIFIED-IO, a model that performs a large variety of AI tasks spanning classical computer vision tasks, including pose estimation, object detection, depth estimation and image generation, vision-and-language tasks such as region captioning and referring expression, to natural language processing tasks such a... | UNIFIED-IO: A UNIFIED MODEL FOR VISION, LANGUAGE, AND MULTI-MODAL TASKS |
d238408056 | How can a reinforcement learning (RL) agent prepare to solve downstream tasks if those tasks are not known a priori? One approach is unsupervised skill discovery, a class of algorithms that learn a set of policies without access to a reward function. Such algorithms bear a close resemblance to representation learning a... | THE INFORMATION GEOMETRY OF UNSUPERVISED REINFORCEMENT LEARNING |
d227016389 | We study the problem of predicting and controlling the future state distribution of an autonomous agent. This problem, which can be viewed as a reframing of goal-conditioned reinforcement learning (RL), is centered around learning a conditional probability density function over future states. Instead of directly estima... | C-LEARNING: LEARNING TO ACHIEVE GOALS VIA RECURSIVE CLASSIFICATION |
d9059612 | Adversarial examples are malicious inputs designed to fool machine learning models. They often transfer from one model to another, allowing attackers to mount black box attacks without knowledge of the target model's parameters. Adversarial training is the process of explicitly training a model on adversarial examples,... | ADVERSARIAL MACHINE LEARNING AT SCALE |
d263334319 | Exploring rich environments and evaluating one's actions without prior knowledge is immensely challenging.In this paper, we propose Motif, a general method to interface such prior knowledge from a Large Language Model (LLM) with an agent.Motif is based on the idea of grounding LLMs for decision-making without requiring... | MOTIF: INTRINSIC MOTIVATION FROM ARTIFICIAL INTELLIGENCE FEEDBACK |
d204788663 | Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning. A Bayes-optimal policy, which does so optimally, conditions its actions not only on the environment state but on the agent's uncertainty about the environment. Computing a Bayes-optimal policy is how... | VARIBAD: A VERY GOOD METHOD FOR BAYES-ADAPTIVE DEEP RL VIA META-LEARNING |
d202889230 | Temporal point processes are the dominant paradigm for modeling sequences of events happening at irregular intervals. The standard way of learning in such models is by estimating the conditional intensity function. However, parameterizing the intensity function usually incurs several trade-offs. We show how to overcome... | INTENSITY-FREE LEARNING OF TEMPORAL POINT PROCESSES |
d5592690 | Machine comprehension of text is an important problem in natural language processing. A recently released dataset, the Stanford Question Answering Dataset (SQuAD), offers a large number of real questions and their answers created by humans through crowdsourcing. SQuAD provides a challenging testbed for evaluating machi... | MACHINE COMPREHENSION USING MATCH-LSTM AND ANSWER POINTER |
d260886874 | Finetuning large language models (LLMs) on instructions leads to vast performance improvements on natural language tasks. We apply instruction tuning using code, leveraging the natural structure of Git commits, which pair code changes with human instructions. We compile COMMITPACK: 4 terabytes of Git commits across 350... | OCTOPACK: INSTRUCTION TUNING CODE LARGE LANGUAGE MODELS |
d252185187 | We study the problem of online low-rank matrix completion with M users, N items and T rounds. In each round, the algorithm recommends one item per user, for which it gets a (noisy) reward sampled from a low-rank user-item preference matrix. The goal is to design a method with sub-linear regret (in T) and nearly optimal... | Online Low Rank Matrix Completion |
d245124492 | Multi-view stereo (MVS) is a crucial task for precise 3D reconstruction. Most recent studies tried to improve the performance of matching cost volume in MVS by designing aggregated 3D cost volumes and their regularization. This paper focuses on learning a robust feature extraction network to enhance the performance of ... | CURVATURE-GUIDED DYNAMIC SCALE NETWORKS FOR MULTI-VIEW STEREO |
d249375516 | Given only positive (P) and unlabeled (U) data, PU learning can train a binary classifier without any negative data. It has two building blocks: PU class-prior estimation (CPE) and PU classification; the latter has been well studied while the former has received less attention. Hitherto, the distributional-assumption-f... | Rethinking Class-Prior Estimation for Positive-Unlabeled Learning |
d248157108 | Code is seldom written in a single left-to-right pass and is instead repeatedly edited and refined. We introduce INCODER, a unified generative model that can perform program synthesis (via left-to-right generation) as well as editing (via masking and infilling). InCoder is trained to generate code files from a large co... | INCODER: A GENERATIVE MODEL FOR CODE INFILLING AND SYNTHESIS |
d235614307 | Deep neural networks are known to be vulnerable to adversarial examples, where a perturbation in the input space leads to an amplified shift in the latent network representation. In this paper, we combine canonical supervised learning with selfsupervised representation learning, and present Self-supervised Online Adver... | ONLINE ADVERSARIAL PURIFICATION BASED ON SELF-SUPERVISED LEARNING |
d263829358 | We study multi-agent reinforcement learning (MARL) for the general-sum Markov Games (MGs) under the general function approximation. In order to find the minimum assumption for sample-efficient learning, we introduce a novel complexity measure called the Multi-Agent Decoupling Coefficient (MADC) for general-sum MGs. Usi... | Sample-Efficient Multi-Agent RL: An Optimization Perspective |
d53783799 | Artistic style transfer is the problem of synthesizing an image with content similar to a given image and style similar to another. Although recent feed-forward neural networks can generate stylized images in real-time, these models produce a single stylization given a pair of style/content images, and the user doesn't... | Adjustable Real-time Style Transfer |
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