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d235422251 | A major challenge in structured prediction is to represent the interdependencies within output structures. When outputs are structured as sequences, linear-chain conditional random fields (CRFs) are a widely used model class which can learn local dependencies in the output. However, the CRF's Markov assumption makes it... | CONSTRAINING LINEAR-CHAIN CRFS TO REGULAR LANGUAGES |
d108300988 | Contextualized representation models such as ELMo(Peters et al., 2018a)andBERT (Devlin et al., 2018)have recently achieved state-of-the-art results on a diverse array of downstream NLP tasks. Building on recent token-level probing work, we introduce a novel edge probing task design and construct a broad suite of sub-se... | WHAT DO YOU LEARN FROM CONTEXT? PROBING FOR SENTENCE STRUCTURE IN CONTEXTUALIZED WORD REPRESENTATIONS |
d252734866 | From human physiology to environmental evolution, important processes in nature often exhibit meaningful and strong periodic or quasi-periodic changes. Due to their inherent label scarcity, learning useful representations for periodic tasks with limited or no supervision is of great benefit. Yet, existing self-supervis... | SIMPER: SIMPLE SELF-SUPERVISED LEARNING OF PERIODIC TARGETS |
d53112107 | Momentum-based acceleration of stochastic gradient descent (SGD) is widely used in deep learning. We propose the quasi-hyperbolic momentum algorithm (QHM) as an extremely simple alteration of momentum SGD, averaging a plain SGD step with a momentum step. We describe numerous connections to and identities with other alg... | QUASI-HYPERBOLIC MOMENTUM AND ADAM FOR DEEP LEARNING |
d256627683 | Privacy estimation techniques for differentially private (DP) algorithms are useful for comparing against analytical bounds, or to empirically measure privacy loss in settings where known analytical bounds are not tight.However, existing privacy auditing techniques usually make strong assumptions on the adversary (e.g.... | One-shot Empirical Privacy Estimation for Federated Learning |
d204823990 | Since deep neural networks are over-parametrized, they may memorize noisy examples. We address such memorizing issue under the existence of annotation noise. From the fact that deep neural networks cannot generalize neighborhoods of the features acquired via memorization, we find that noisy examples do not consistently... | ROBUST TRAINING WITH ENSEMBLE CONSENSUS |
d209862298 | A deep neural network (DNN) with piecewise linear activations can partition the input space into numerous small linear regions, where different linear functions are fitted. It is believed that the number of these regions represents the expressivity of the DNN. This paper provides a novel and meticulous perspective to l... | EMPIRICAL STUDIES ON THE PROPERTIES OF LINEAR REGIONS IN DEEP NEURAL NETWORKS |
d237581512 | Molecule representation learning (MRL) methods aim to embed molecules into a real vector space. However, existing SMILES-based (Simplified Molecular-Input Line-Entry System) or GNN-based (Graph Neural Networks) MRL methods either take SMILES strings as input that have difficulty in encoding molecule structure informati... | CHEMICAL-REACTION-AWARE MOLECULE REPRESENTATION LEARNING |
d245650881 | In many reinforcement learning (RL) applications, the observation space is specified by human developers and restricted by physical realizations, and may thus be subject to dramatic changes over time (e.g. increased number of observable features). However, when the observation space changes, the previous policy will li... | TRANSFER RL ACROSS OBSERVATION FEATURE SPACES VIA MODEL-BASED REGULARIZATION |
d246430387 | Counterfactual explanations are attracting significant attention due to the flourishing applications of machine learning models in consequential domains. A counterfactual plan consists of multiple possibilities to modify a given instance so that the model's prediction will be altered. As the predictive model can be upd... | COUNTERFACTUAL PLANS UNDER DISTRIBUTIONAL AMBIGUITY |
d252780502 | Identifying near duplicates within large, noisy text corpora has a myriad of applications that range from de-duplicating training datasets, reducing privacy risk, and evaluating test set leakage, to identifying reproduced news articles and literature within large corpora. Across these diverse applications, the overwhel... | Noise-Robust De-Duplication at Scale |
d26103652 | Graphs are a prevalent tool in data science, as they model the inherent structure of the data. They have been used successfully in unsupervised and semi-supervised learning. Typically they are constructed either by connecting nearest samples, or by learning them from data, solving an optimization problem. While graph l... | Large Scale Graph Learning from Smooth Signals |
d53327717 | Learning a deep neural network requires solving a challenging optimization problem: it is a high-dimensional, non-convex and non-smooth minimization problem with a large number of terms. The current practice in neural network optimization is to rely on the stochastic gradient descent (SGD) algorithm or its adaptive var... | DEEP FRANK-WOLFE FOR NEURAL NETWORK OPTIMIZATION |
d253734748 | Point process data are becoming ubiquitous in modern applications, such as social networks, health care, and finance. Despite the powerful expressiveness of the popular recurrent neural network (RNN) models for point process data, they may not successfully capture sophisticated non-stationary dependencies in the data d... | Spatio-temporal point processes with deep non-stationary kernels |
d238354065 | Recent developments in large-scale machine learning suggest that by scaling up data, model size and training time properly, one might observe that improvements in pre-training would transfer favorably to most downstream tasks. In this work, we systematically study this phenomena and establish that, as we increase the u... | Exploring the Limits of Large Scale Pre-training |
d249191952 | Domain Adaptation of Black-box Predictors (DABP) aims to learn a model on an unlabeled target domain supervised by a black-box predictor trained on a source domain. It does not require access to both the source-domain data and the predictor parameters, thus addressing the data privacy and portability issues of standard... | Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors |
d252596087 | Research on text-to-image generation has witnessed significant progress in generating diverse and photo-realistic images, driven by diffusion and auto-regressive models trained on large-scale image-text data. Though state-of-the-art models can generate high-quality images of common entities, they often have difficulty ... | RE-IMAGEN: RETRIEVAL-AUGMENTED TEXT-TO-IMAGE GENERATOR |
d18828233 | The goal of this paper is not to introduce a single algorithm or method, but to make theoretical steps towards fully understanding the training dynamics of generative adversarial networks. In order to substantiate our theoretical analysis, we perform targeted experiments to verify our assumptions, illustrate our claims... | TOWARDS PRINCIPLED METHODS FOR TRAINING GENERATIVE ADVERSARIAL NETWORKS |
d249712407 | Recent empirical studies on domain generalization (DG) have shown that DG algorithms that perform well on some distribution shifts fail on others, and no state-of-the-art DG algorithm performs consistently well on all shifts. Moreover, real-world data often has multiple distribution shifts over different attributes; he... | MODELING THE DATA-GENERATING PROCESS IS NECESSARY FOR OUT-OF-DISTRIBUTION GENERALIZATION |
d3474523 | The development of high-dimensional generative models has recently gained a great surge of interest with the introduction of variational auto-encoders and generative adversarial neural networks. Different variants have been proposed where the underlying latent space is structured, for example, based on attributes descr... | MULTI-VIEW DATA GENERATION WITHOUT VIEW SUPERVISION |
d261557296 | Speech conveys more information than text, as the same word can be uttered in various voices to convey diverse information. Compared to traditional text-to-speech (TTS) methods relying on speech prompts (reference speech) for voice variability, using text prompts (descriptions) is more user-friendly since speech prompt... | PROMPTTTS 2: DESCRIBING AND GENERATING VOICES WITH TEXT PROMPT |
d239616032 | We develop a fast and reliable method for solving large-scale optimal transport (OT) problems at an unprecedented combination of speed and accuracy. Built on the celebrated Douglas-Rachford splitting technique, our method tackles the original OT problem directly instead of solving an approximate regularized problem, as... | A Fast and Accurate Splitting Method for Optimal Transport: Analysis and Implementation |
d252735281 | Modern deep learning involves training costly, highly overparameterized networks, thus motivating the search for sparser networks that require less compute and memory but can still be trained to the same accuracy as the full network (i.e. matching). Iterative magnitude pruning (IMP) is a state of the art algorithm that... | Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? |
d246863713 | Diffusion models have recently outperformed alternative approaches to model the distribution of natural images. Such diffusion models allow for deterministic sampling via the probability flow ODE, giving rise to a latent space and an encoder map. While having important practical applications, such as the estimation of ... | UNDERSTANDING DDPM LATENT CODES THROUGH OPTIMAL TRANSPORT |
d248811614 | Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization. However, we argue that existing methods might fail to utilize the unlabeled data more effectively since they either use a pre-defined / fixe... | FREEMATCH: SELF-ADAPTIVE THRESHOLDING FOR SEMI-SUPERVISED LEARNING |
d264832900 | State-of-the-art systems neuroscience experiments yield large-scale multimodal data, and these data sets require new tools for analysis.Inspired by the success of large pretrained models in vision and language domains, we reframe the analysis of large-scale, cellular-resolution neuronal spiking data into an autoregress... | NEUROFORMER: MULTIMODAL AND MULTITASK GENERATIVE PRETRAINING FOR BRAIN DATA |
d15630723 | Most machine learning applications using neural networks seek to approximate some function g(x) by minimizing some cost criterion. In the simplest case, if one has access to pairs of the form (x, y) where y = g(x), the problem can be framed as a regression problem. Beyond this family of problems, we find many cases whe... | RECURSIVE REGRESSION WITH NEURAL NETWORKS: APPROXIMATING THE HJI PDE SOLUTION |
d15872881 | The optimization of deep neural networks can be more challenging than traditional convex optimization problems due to the highly non-convex nature of the loss function, e.g. it can involve pathological landscapes such as saddle-surfaces that can be difficult to escape for algorithms based on simple gradient descent. In... | Mollifying Networks |
d251554821 | Offline reinforcement learning (RL), which aims to learn an optimal policy using a previously collected static dataset, is an important paradigm of RL. Standard RL methods often perform poorly in this regime due to the function approximation errors on out-of-distribution actions. While a variety of regularization metho... | DIFFUSION POLICIES AS AN EXPRESSIVE POLICY CLASS FOR OFFLINE REINFORCEMENT LEARNING |
d225076297 | There have been growing concerns regarding the fabrication of contents through generative models. This paper investigates the feasibility of decentralized attribution of such models. Given a set of generative models learned from the same dataset, attributability is achieved when a public verification service exists to ... | DECENTRALIZED ATTRIBUTION OF GENERATIVE MODELS |
d239049743 | Discovery and learning of an underlying spatiotemporal hierarchy in sequential data is an important topic for machine learning. Despite this, little work has been done to explore hierarchical generative models that can flexibly adapt their layerwise representations in response to datasets with different temporal dynami... | VARIATIONAL PREDICTIVE ROUTING WITH NESTED SUBJECTIVE TIMESCALES |
d263152444 | Motion prediction is crucial for autonomous vehicles to operate safely in complex traffic environments.Extracting effective spatiotemporal relationships among traffic elements is key to accurate forecasting.Inspired by the successful practice of pretrained large language models, this paper presents SEPT, a modeling fra... | SEPT: TOWARDS EFFICIENT SCENE REPRESENTATION LEARNING FOR MOTION PREDICTION |
d222124972 | We propose a fast, distance-preserving, binary embedding algorithm to transform a high-dimensional dataset T ⊆ R n into binary sequences in the cube {±1} m . When T consists of well-spread (i.e., non-sparse) vectors, our embedding method applies a stable noise-shaping quantization scheme to Ax where A ∈ R m×n is a spar... | FASTER BINARY EMBEDDINGS FOR PRESERVING EUCLIDEAN DISTANCES |
d257532608 | One of the main motivations of studying continual learning is that the problem setting allows a model to accrue knowledge from past tasks to learn new tasks more efficiently. However, recent studies suggest that the key metric that continual learning algorithms optimize, reduction in catastrophic forgetting, does not c... | IS FORGETTING LESS A GOOD INDUCTIVE BIAS FOR FORWARD TRANSFER? |
d263835075 | Posterior sampling allows the exploitation of prior knowledge of the environment's transition dynamics to improve the sample efficiency of reinforcement learning. The prior is typically specified as a class of parametric distributions, a task that can be cumbersome in practice, often resulting in the choice of uninform... | Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning |
d264306078 | With the development of large language models (LLMs), striking a balance between the performance and safety of AI systems has never been more critical.However, the inherent tension between the objectives of helpfulness and harmlessness presents a significant challenge during LLM training.To address this issue, we propo... | SAFE RLHF: SAFE REINFORCEMENT LEARNING FROM HUMAN FEEDBACK |
d263831485 | Foundation models in language and vision have the ability to run inference on any textual and visual inputs thanks to the transferable representations such as a vocabulary of tokens in language.Knowledge graphs (KGs) have different entity and relation vocabularies that generally do not overlap.The key challenge of desi... | TOWARDS FOUNDATION MODELS FOR KNOWLEDGE GRAPH REASONING |
d256194627 | Several recent works on self-supervised learning are trained by mapping different augmentations of the same image to the same feature representation. The data augmentations used are of crucial importance to the quality of learned feature representations. In this paper, we analyze how the color jitter traditionally used... | Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training |
d221878944 | We study how neural networks trained by gradient descent extrapolate, i.e., what they learn outside the support of training distribution. Previous works report mixed empirical results when extrapolating with neural networks: while multilayer perceptrons (MLPs) do not extrapolate well in simple tasks, Graph Neural Netwo... | How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks |
d67915085 | Sorting input objects is an important step in many machine learning pipelines. However, the sorting operator is non-differentiable with respect to its inputs, which prohibits end-to-end gradient-based optimization. In this work, we propose NeuralSort, a general-purpose continuous relaxation of the output of the sorting... | STOCHASTIC OPTIMIZATION OF SORTING NETWORKS VIA CONTINUOUS RELAXATIONS |
d211026455 | The composition of elementary behaviors to solve challenging transfer learning problems is one of the key elements in building intelligent machines. To date, there has been plenty of work on learning task-specific policies or skills but almost no focus on composing necessary, task-agnostic skills to find a solution to ... | COMPOSING TASK-AGNOSTIC POLICIES WITH DEEP REINFORCEMENT LEARNING |
d5044692 | Recurrent neural networks (RNNs) have achieved state-of-the-art performance on many diverse tasks, from machine translation to surgical activity recognition, yet training RNNs to capture long-term dependencies remains difficult. To date, the vast majority of successful RNN architectures alleviate this problem by facili... | Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies |
d235829223 | A variety of recent works, spanning pruning, lottery tickets, and training within random subspaces, have shown that deep neural networks can be trained using far fewer degrees of freedom than the total number of parameters. We analyze this phenomenon for random subspaces by first examining the success probability of hi... | HOW MANY DEGREES OF FREEDOM DO WE NEED TO TRAIN DEEP NETWORKS: A LOSS LANDSCAPE PERSPECTIVE |
d220647137 | Over-parameterized models, in particular deep networks, often exhibit a double descent phenomenon, where as a function of model size, error first decreases, increases, and decreases at last. This intriguing double descent behavior also occurs as a function of training epochs, and has been conjectured to arise because t... | Early Stopping in Deep Networks: Double Descent and How to Eliminate it |
d208527831 | The practical usage of reinforcement learning agents is often bottlenecked by the duration of training time. To accelerate training, practitioners often turn to distributed reinforcement learning architectures to parallelize and accelerate the training process. However, modern methods for scalable reinforcement learnin... | IMPACT: IMPORTANCE WEIGHTED ASYNCHRONOUS ARCHITECTURES WITH CLIPPED TARGET NETWORKS |
d263831492 | The demonstrated code-understanding capability of LLMs raises the question of whether they can be used for automated program verification, a task that often demands high-level abstract reasoning about program properties that is challenging for verification tools.We propose a general methodology to combine the power of ... | LEMUR: INTEGRATING LARGE LANGUAGE MODELS IN AUTOMATED PROGRAM VERIFICATION |
d220525353 | As modern neural networks have grown to billions of parameters, meeting tight latency budgets has become increasingly challenging. Approaches like compression, sparsification and network pruning have proven effective to tackle this problembut they rely on modifications of the underlying network. In this paper, we look ... | Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning |
d248377629 | While deep reinforcement learning methods have shown impressive results in robot learning, their sample inefficiency makes the learning of complex, longhorizon behaviors with real robot systems infeasible. To mitigate this issue, metareinforcement learning methods aim to enable fast learning on novel tasks by learning ... | SKILL-BASED META-REINFORCEMENT LEARNING |
d59413789 | We propose to meta-learn causal structures based on how fast a learner adapts to new distributions arising from sparse distributional changes, e.g. due to interventions, actions of agents and other sources of non-stationarities. We show that under this assumption, the correct causal structural choices lead to faster ad... | A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms |
d259924554 | The deployment of Reinforcement Learning (RL) in real-world applications is constrained by its failure to satisfy safety criteria.Existing Safe Reinforcement Learning (SafeRL) methods, which rely on cost functions to enforce safety, often fail to achieve zero-cost performance in complex scenarios, especially vision-onl... | SAFEDREAMER: SAFE REINFORCEMENT LEARNING WITH WORLD MODELS |
d222133972 | Given a large data matrix, sparsifying, quantizing, and/or performing other entry-wise nonlinear operations can have numerous benefits, ranging from speeding up iterative algorithms for core numerical linear algebra problems to providing nonlinear filters to design state-of-the-art neural network models. Here, we explo... | Sparse Quantized Spectral Clustering |
d231639283 | A recent line of work showed that various forms of convolutional kernel methods can be competitive with standard supervised deep convolutional networks on datasets like CIFAR-10, obtaining accuracies in the range of 87 − 90% while being more amenable to theoretical analysis. In this work, we highlight the importance of... | THE UNREASONABLE EFFECTIVENESS OF PATCHES IN DEEP CONVOLUTIONAL KERNELS METHODS |
d258865613 | Reasoning on knowledge graphs is a challenging task because it utilizes observed information to predict the missing one.Particularly, answering complex queries based on first-order logic is one of the crucial tasks to verify learning to reason abilities for generalization and composition.Recently, the prevailing method... | Rethinking Complex Queries on Knowledge Graphs with Neural Link Predictors |
d246679973 | In conventional object detection frameworks, a backbone body inherited from image recognition models extracts deep latent features and then a neck module fuses these latent features to capture information at different scales. As the resolution in object detection is much larger than in image recognition, the computatio... | GIRAFFEDET: A HEAVY-NECK PARADIGM FOR OBJECT DETECTION |
d245334837 | Recent work has shown that supervised learning alone, without temporal difference (TD) learning, can be remarkably effective for offline RL. When does this hold true, and which algorithmic components are necessary? Through extensive experiments, we boil supervised learning for offline RL down to its essential elements.... | RVS: WHAT IS ESSENTIAL FOR OFFLINE RL VIA SUPERVISED LEARNING? |
d251647974 | A key assumption in multi-task learning is that at the inference time the multi-task model only has access to a given data point but not to the data point's labels from other tasks. This presents an opportunity to extend multi-task learning to utilize data point's labels from other auxiliary tasks, and this way improve... | RELATIONAL MULTI-TASK LEARNING: MODELING RELATIONS BETWEEN DATA AND TASKS |
d263620587 | Machine learning approaches relying on such criteria as adversarial robustness or multi-agent settings have raised the need for solving game-theoretic equilibrium problems.Of particular relevance to these applications are methods targeting finite-sum structure, which generically arises in empirical variants of learning... | Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions |
d258833680 | This paper introduces a new method for minimizing matrix-smooth non-convex objectives through the use of novel Compressed Gradient Descent (CGD) algorithms enhanced with a matrix-valued stepsize. The proposed algorithms are theoretically analyzed first in the single-node and subsequently in the distributed settings. Ou... | Det-CGD: Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization |
d263830786 | The popularity of LLaMA(Touvron et al., 2023a;b) and other recently emerged moderate-sized large language models (LLMs) highlights the potential of building smaller yet powerful LLMs.Regardless, the cost of training such models from scratch on trillions of tokens remains high.In this work, we study structured pruning ... | SHEARED LLAMA: ACCELERATING LANGUAGE MODEL PRE-TRAINING VIA STRUCTURED PRUNING |
d2331610 | Techniques such as ensembling and distillation promise model quality improvements when paired with almost any base model. However, due to increased testtime cost (for ensembles) and increased complexity of the training pipeline (for distillation), these techniques are challenging to use in industrial settings. In this ... | LARGE SCALE DISTRIBUTED NEURAL NETWORK TRAINING THROUGH ONLINE DISTILLATION |
d58004637 | Deep neural networks work well at approximating complicated functions when provided with data and trained by gradient descent methods. At the same time, there is a vast amount of existing functions that programmatically solve different tasks in a precise manner eliminating the need for training. In many cases, it is po... | NEURAL NETWORK GRADIENT-BASED LEARNING OF BLACK-BOX FUNCTION INTERFACES |
d256697567 | Switching costs, which capture the costs for changing policies, are regarded as a critical metric in reinforcement learning (RL), in addition to the standard metric of losses (or rewards). However, existing studies on switching costs (with a coefficient β that is strictly positive and is independent of T ) have mainly ... | NEAR-OPTIMAL ADVERSARIAL REINFORCEMENT LEARNING WITH SWITCHING COSTS |
d246823327 | The success of multi-head self-attentions (MSAs) for computer vision is now indisputable. However, little is known about how MSAs work. We present fundamental explanations to help better understand the nature of MSAs. In particular, we demonstrate the following properties of MSAs and Vision Transformers (ViTs): 1 MSAs ... | HOW DO VISION TRANSFORMERS WORK? |
d248496446 | Objects' motions in nature are governed by complex interactions and their properties. While some properties, such as shape and material, can be identified via the object's visual appearances, others like mass and electric charge are not directly visible. The compositionality between the visible and hidden properties po... | COMPHY: COMPOSITIONAL PHYSICAL REASONING OF OBJECTS AND EVENTS FROM VIDEOS |
d249240475 | Muscle-actuated organisms are capable of learning an unparalleled diversity of dexterous movements despite their vast amount of muscles. Reinforcement learning (RL) on large musculoskeletal models, however, has not been able to show similar performance. We conjecture that ineffective exploration in large overactuated a... | DEP-RL: EMBODIED EXPLORATION FOR REINFORCEMENT LEARNING IN OVERACTUATED AND MUSCULOSKELETAL SYSTEMS |
d235485330 | Deep neural networks (DNNs) are effective in solving many real-world problems. Larger DNN models usually exhibit better quality (e.g., accuracy) but their excessive computation results in long inference time. Model sparsification can reduce the computation and memory cost while maintaining model quality. Most existing ... | EFFECTIVE MODEL SPARSIFICATION BY SCHEDULED GROW-AND-PRUNE METHODS |
d258041281 | Human visual recognition is a sparse process, where only a few salient visual cues are attended to rather than traversing every detail uniformly.However, most current vision networks follow a dense paradigm, processing every single visual unit (e.g., pixel or patch) in a uniform manner.In this paper, we challenge this ... | SparseFormer: Sparse Visual Recognition via Limited Latent Tokens |
d231639158 | Hyperparameter optimization (HPO) is a central pillar in the automation of machine learning solutions and is mainly performed via Bayesian optimization, where a parametric surrogate is learned to approximate the black box response function (e.g. validation error). Unfortunately, evaluating the response function is comp... | FEW-SHOT BAYESIAN OPTIMIZATION WITH DEEP KERNEL SURROGATES |
d53792719 | We aim to build complex humanoid agents that integrate perception, motor control, and memory. In this work, we partly factor this problem into low-level motor control from proprioception and high-level coordination of the low-level skills informed by vision. We develop an architecture capable of surprisingly flexible, ... | HIERARCHICAL VISUOMOTOR CONTROL OF HUMANOIDS |
d220525799 | Real-world classification problems typically exhibit an imbalanced or long-tailed label distribution, wherein many labels are associated with only a few samples. This poses a challenge for generalisation on such labels, and also makes naïve learning biased towards dominant labels. In this paper, we present two simple m... | Long-Tail Learning via Logit Adjustment |
d53841789 | Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research. We present an unsupervised learning algorithm to train agents to achieve perceptuallyspecified goals using only a stream of observations and actions. Our agent si... | UNSUPERVISED CONTROL THROUGH NON-PARAMETRIC DISCRIMINATIVE REWARDS |
d264590713 | In the field of algorithmic fairness, significant attention has been put on group fairness criteria, such as Demographic Parity and Equalized Odds.Nevertheless, these objectives, measured as global averages, have raised concerns about persistent local disparities between sensitive groups.In this work, we address the pr... | ON THE FAIRNESS ROAD: ROBUST OPTIMIZATION FOR ADVERSARIAL DEBIASING |
d254018196 | In distributed computing, slower nodes (stragglers) usually become a bottleneck. Gradient Coding (GC), introduced by Tandon et al., is an efficient technique that uses principles of error-correcting codes to distribute gradient computation in the presence of stragglers. In this paper, we consider the distributed comput... | SEQUENTIAL GRADIENT CODING FOR STRAGGLER MITIGATION |
d259841489 | 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 |
d259064088 | A prominent challenge of offline reinforcement learning (RL) is the issue of hidden confounding: unobserved variables may influence both the actions taken by the agent and the observed outcomes. Hidden confounding can compromise the validity of any causal conclusion drawn from data and presents a major obstacle to effe... | Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding |
d3534906 | While many active learning papers assume that the learner can simply ask for a label and receive it, real annotation often presents a mismatch between the form of a label (say, one among many classes), and the form of an annotation (typically yes/no binary feedback). To annotate examples corpora for multiclass classifi... | Active Learning with Partial Feedback |
d14570343 | Generative adversarial networks (GANs) provide an algorithmic framework for constructing generative models with several appealing properties: they do not require a likelihood function to be specified, only a generating procedure; they provide samples that are sharp and compelling; and they allow us to harness our knowl... | Learning in Implicit Generative Models |
d31004450 | Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and speech recognition. Performance has further been improved by leveraging unlabeled data, often in the form of a language model. In this work, w... | Cold Fusion: Training Seq2Seq Models Together with Language Models |
d3484654 | Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on a novel task. Bayesian hierarchical modeling provides a theoretical framework for formalizing meta-learning as inference for a set of parameters that are shared across tasks. Here, we reformulat... | RECASTING GRADIENT-BASED META-LEARNING AS HIERARCHICAL BAYES |
d235353039 | A highly desirable property of a reinforcement learning (RL) agent -and a major difficulty for deep RL approaches -is the ability to generalize policies learned on a few tasks over a high-dimensional observation space to similar tasks not seen during training. Many promising approaches to this challenge consider RL as ... | CROSS-TRAJECTORY REPRESENTATION LEARNING FOR ZERO-SHOT GENERALIZATION IN RL |
d85466956 | The key attribute that drives the unprecedented success of modern Recurrent Neural Networks (RNNs) on learning tasks which involve sequential data, is their ever-improving ability to model intricate long-term temporal dependencies. However, a well established measure of RNNs long-term memory capacity is lacking, and th... | BENEFITS OF DEPTH FOR LONG-TERM MEMORY OF RECURRENT NETWORKS |
d44143558 | Domain adaptation for visual recognition has undergone great progress in the past few years. Nevertheless, most existing methods work in the so-called closed-set scenario, assuming that the classes depicted by the target images are exactly the same as those of the source domain. In this paper, we tackle the more challe... | Learning Factorized Representations for Open-set Domain Adaptation |
d208617304 | Deep neural networks (DNNs) are poorly calibrated when trained in conventional ways. To improve confidence calibration of DNNs, we propose a novel training method, distance-based learning from errors (DBLE). DBLE bases its confidence estimation on distances in the representation space. In DBLE, we first adapt prototypi... | DISTANCE-BASED LEARNING FROM ERRORS FOR CONFIDENCE CALIBRATION |
d239769246 | We characterize the power-law asymptotics of learning curves for Gaussian process regression (GPR) under the assumption that the eigenspectrum of the prior and the eigenexpansion coefficients of the target function follow a power law. Under similar assumptions, we leverage the equivalence between GPR and kernel ridge r... | Learning curves for Gaussian process regression with power-law priors and targets |
d52979229 | Making deep convolutional neural networks more accurate typically comes at the cost of increased computational and memory resources. In this paper, we exploit the fact that the importance of features computed by convolutional layers is highly input-dependent, and propose feature boosting and suppression (FBS), a new me... | Dynamic Channel Pruning: Feature Boosting and Suppression |
d252683871 | Deep generative models parametrized up to a normalizing constant (e.g. energy-based models) are difficult to train by maximizing the likelihood of the data because the likelihood and/or gradients thereof cannot be explicitly or efficiently written down. Score matching is a training method, whereby instead of fitting th... | Statistical Efficiency of Score Matching: The View from Isoperimetry |
d228376209 | Flow-based models are powerful tools for designing probabilistic models with tractable density. This paper introduces Convex Potential Flows (CP-Flow), a natural and efficient parameterization of invertible models inspired by the optimal transport (OT) theory. CP-Flows are the gradient map of a strongly convex neural p... | CONVEX POTENTIAL FLOWS: UNIVERSAL PROBABILITY DISTRIBUTIONS WITH OPTIMAL TRANSPORT AND CONVEX OPTIMIZATION |
d246430821 | There have been growing interests in leveraging experimental measurements to discover the underlying partial differential equations (PDEs) that govern complex physical phenomena. Although past research attempts have achieved great success in data-driven PDE discovery, the robustness of the existing methods cannot be gu... | DISCOVERING NONLINEAR PDES FROM SCARCE DATA WITH PHYSICS-ENCODED LEARNING |
d211171892 | We investigate approaches to regularisation during fine-tuning of deep neural networks. First we provide a neural network generalisation bound based on Rademacher complexity that uses the distance the weights have moved from their initial values. This bound has no direct dependence on the number of weights and compares... | Distance-Based Regularisation of Deep Networks for Fine-Tuning |
d231592776 | Reinforcement learning methods trained on few environments rarely learn policies that generalize to unseen environments. To improve generalization, we incorporate the inherent sequential structure in reinforcement learning into the representation learning process. This approach is orthogonal to recent approaches, which... | CONTRASTIVE BEHAVIORAL SIMILARITY EMBEDDINGS FOR GENERALIZATION IN REINFORCEMENT LEARNING |
d3329316 | To train an inference network jointly with a deep generative topic model, making it both scalable to big corpora and fast in out-of-sample prediction, we develop Weibull hybrid autoencoding inference (WHAI) for deep latent Dirichlet allocation, which infers posterior samples via a hybrid of stochastic-gradient MCMC and... | WHAI: WEIBULL HYBRID AUTOENCODING INFERENCE FOR DEEP TOPIC MODELING |
d1890353 | Neural Networks are function approximators that have achieved state-of-the-art accuracy in numerous machine learning tasks. In spite of their great success in terms of accuracy, their large training time makes it difficult to use them for various tasks. In this paper, we explore the idea of learning weight evolution pa... | INTROSPECTION:ACCELERATING NEURAL NETWORK TRAINING BY LEARNING WEIGHT EVOLUTION |
d258823075 | Maximum mean discrepancy (MMD) flows suffer from high computational costs in large scale computations.In this paper, we show that MMD flows with Riesz kernels K(x, y) = −∥x − y∥ r , r ∈ (0, 2) have exceptional properties which allow their efficient computation.We prove that the MMD of Riesz kernels, which is also known... | Generative Sliced MMD Flows with Riesz Kernels |
d256390009 | We explore the problem of generating minority samples using diffusion models. The minority samples are instances that lie on low-density regions of a data manifold. Generating sufficient numbers of such minority instances is important, since they often contain some unique attributes of the data. However, the convention... | Don't Play Favorites: Minority Guidance for Diffusion Models |
d235436185 | We introduce a self-supervised vision representation model BEIT, which stands for Bidirectional Encoder representation from Image Transformers. Following BERT [DCLT19] developed in the natural language processing area, we propose a masked image modeling task to pretrain vision Transformers. Specifically, each image has... | BEIT: BERT Pre-Training of Image Transformers |
d263333921 | This paper explores network binarization, a radical form of quantization, compressing model weights to a single bit, specifically for Large Language Models (LLMs) compression.Due to previous binarization methods collapsing LLMs, we propose a novel approach, Partially-Binarized LLM (PB-LLM), which can achieve extreme lo... | PB-LLM: PARTIALLY BINARIZED LARGE LANGUAGE MODELS |
d257636556 | When a small number of poisoned samples are injected into the training dataset of a deep neural network, the network can be induced to exhibit malicious behavior during inferences, which poses potential threats to real-world applications. While they have been intensively studied in classification, backdoor attacks on s... | Influencer Backdoor Attack on Semantic Segmentation |
d226227077 | A key challenge for deploying deep neural networks (DNNs) in safety critical settings is the need to provide rigorous ways to quantify their uncertainty. In this paper, we propose a novel algorithm for constructing predicted classification confidences for DNNs that comes with provable correctness guarantees. Our approa... | PAC CONFIDENCE PREDICTIONS FOR DEEP NEURAL NETWORK CLASSIFIERS |
d249538526 | Knowledge graphs are inherently incomplete. Therefore substantial research has been directed toward knowledge graph completion (KGC), i.e., predicting missing triples from the information represented in the knowledge graph (KG). KG embedding models (KGEs) have yielded promising results for KGC, yet any current KGE is i... | EXPRESSIVE: A SPATIO-FUNCTIONAL EMBEDDING FOR KNOWLEDGE GRAPH COMPLETION |
d3652072 | Multiagent systems where the agents interact among themselves and with an stochastic environment can be formalized as stochastic games. We study a subclass of these games, named Markov potential games (MPGs), that appear often in economic and engineering applications when the agents share some common resource. We consi... | LEARNING PARAMETRIC CLOSED-LOOP POLICIES FOR MARKOV POTENTIAL GAMES |
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