Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
id stringlengths 3 9 | title stringlengths 0 8.56k | text stringlengths 0 18.4k |
|---|---|---|
252715594 | PHENAKI: VARIABLE LENGTH VIDEO GENERATION FROM OPEN DOMAIN TEXTUAL DESCRIPTIONS | We present Phenaki, a model capable of realistic video synthesis, given a sequence of textual prompts. Generating videos from text is particularly challenging due to the computational cost, limited quantities of high quality text-video data and variable length of videos. To address these issues, we introduce a new mode... |
13002849 | MODE REGULARIZED GENERATIVE ADVERSARIAL NETWORKS | Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue that these bad behaviors of GANs are due to the very particular functional shape of the trained discriminators in high dimensional spaces, wh... |
239998253 | What Do We Mean by Generalization in Federated Learning? | Federated learning data is drawn from a distribution of distributions: clients are drawn from a meta-distribution, and their data are drawn from local data distributions. Thus generalization studies in federated learning should separate performance gaps from unseen client data (out-of-sample gap) from performance gaps ... |
62841605 | SPREADING VECTORS FOR SIMILARITY SEARCH | Discretizing multi-dimensional data distributions is a fundamental step of modern indexing methods. State-of-the-art techniques learn parameters of quantizers on training data for optimal performance, thus adapting quantizers to the data. In this work, we propose to reverse this paradigm and adapt the data to the quant... |
253237531 | MACHINE UNLEARNING OF FEDERATED CLUSTERS | Federated clustering (FC) is an unsupervised learning problem that arises in a number of practical applications, including personalized recommender and healthcare systems. With the adoption of recent laws ensuring the "right to be forgotten", the problem of machine unlearning for FC methods has become of significant im... |
222291443 | CONTRASTIVE EXPLANATIONS FOR REINFORCEMENT LEARNING VIA EMBEDDED SELF PREDICTIONS | We investigate a deep reinforcement learning (RL) architecture that supports explaining why a learned agent prefers one action over another. The key idea is to learn action-values that are directly represented via human-understandable properties of expected futures. This is realized via the embedded self-prediction (ES... |
223956716 | FOR SELF-SUPERVISED LEARNING, RATIONALITY IMPLIES GENERALIZATION, PROVABLY | We prove a new upper bound on the generalization gap of classifiers that are obtained by first using self-supervision to learn a representation r of the training data, and then fitting a simple (e.g., linear) classifier g to the labels. Specifically, we show that (under the assumptions described below) the generalizati... |
263605472 | MULTI-TASK LEARNING WITH 3D-AWARE REGULARIZATION | Deep neural networks have become a standard building block for designing models that can perform multiple dense computer vision tasks such as depth estimation and semantic segmentation thanks to their ability to capture complex correlations in high dimensional feature space across tasks.However, the cross-task correlat... |
212996548 | LITE TRANSFORMER WITH LONG-SHORT RANGE ATTENTION | Transformer has become ubiquitous in natural language processing (e.g., machine translation, question answering); however, it requires enormous amount of computations to achieve high performance, which makes it not suitable for mobile applications that are tightly constrained by the hardware resources and battery. In t... |
202719276 | ROBUST LOCAL FEATURES FOR IMPROVING THE GENERALIZATION OF ADVERSARIAL TRAINING | Adversarial training has been demonstrated as one of the most effective methods for training robust models so as to defend against adversarial examples. However, adversarial training often lacks adversarially robust generalization on unseen data. Recent works show that adversarially trained models may be more biased to... |
220665539 | Randomized Automatic Differentiation | The successes of deep learning, variational inference, and many other fields have been aided by specialized implementations of reverse-mode automatic differentiation (AD) to compute gradients of mega-dimensional objectives. The AD techniques underlying these tools were designed to compute exact gradients to numerical p... |
263152628 | 3D RECONSTRUCTION WITH GENERALIZABLE NEURAL FIELDS USING SCENE PRIORS | High-fidelity 3D scene reconstruction has been substantially advanced by recent progress in neural fields. However, most existing methods train a separate network from scratch for each individual scene. This is not scalable, inefficient, and unable to yield good results given limited views. While learning-based multi-v... |
264802502 | OFFLINE RL WITH OBSERVATION HISTORIES: ANALYZING AND IMPROVING SAMPLE COMPLEXITY | Offline reinforcement learning (RL) can in principle synthesize more optimal behavior from a dataset consisting only of suboptimal trials.One way that this can happen is by "stitching" together the best parts of otherwise suboptimal trajectories that overlap on similar states, to create new behaviors where each individ... |
227068701 | LEARNING ENERGY-BASED MODELS BY DIFFUSION RECOVERY LIKELIHOOD | While energy-based models (EBMs) exhibit a number of desirable properties, training and sampling on high-dimensional datasets remains challenging. Inspired by recent progress on diffusion probabilistic models, we present a diffusion recovery likelihood method to tractably learn and sample from a sequence of EBMs traine... |
251732759 | ENERGY-INSPIRED SELF-SUPERVISED PRETRAINING FOR VISION MODELS | Motivated by the fact that forward and backward passes of a deep network naturally form symmetric mappings between input and output representations, we introduce a simple yet effective self-supervised vision model pretraining framework inspired by energy-based models (EBMs). In the proposed framework, we model energy e... |
253523474 | CHARACTERIZING THE SPECTRUM OF THE NTK VIA A POWER SERIES EXPANSION | Under mild conditions on the network initialization we derive a power series expansion for the Neural Tangent Kernel (NTK) of arbitrarily deep feedforward networks in the infinite width limit. We provide expressions for the coefficients of this power series which depend on both the Hermite coefficients of the activatio... |
162184036 | DURATION-OF-STAY STORAGE ASSIGNMENT UNDER UNCERTAINTY | Storage assignment, the act of choosing what goods are placed in what locations in a warehouse, is a central problem of supply chain logistics. Past literature has shown that the optimal method to assign pallets is to arrange them in increasing duration of stay (DoS) in the warehouse (the DoS method), but the methodolo... |
264555396 | IMPROVING INTRINSIC EXPLORATION BY CREATING STATIONARY OBJECTIVES | Exploration bonuses in reinforcement learning guide long-horizon exploration by defining custom intrinsic objectives.Count-based methods use the frequency of state visits to derive an exploration bonus.In this paper, we identify that any intrinsic reward function derived from count-based methods is non-stationary and h... |
251341969 | DYNAMIC UPDATE-TO-DATA RATIO: MINIMIZING WORLD MODEL OVERFITTING | Early stopping based on the validation set performance is a popular approach to find the right balance between under-and overfitting in the context of supervised learning. However, in reinforcement learning, even for supervised sub-problems such as world model learning, early stopping is not applicable as the dataset i... |
255340742 | Delving into Semantic Scale Imbalance | Model bias triggered by long-tailed data has been widely studied. However, measure based on the number of samples cannot explicate three phenomena simultaneously: (1) Given enough data, the classification performance gain is marginal with additional samples. (2) Classification performance decays precipitously as the nu... |
53467348 | FEATURE-WISE BIAS AMPLIFICATION | We study the phenomenon of bias amplification in classifiers, wherein a machine learning model learns to predict classes with a greater disparity than the underlying ground truth. We demonstrate that bias amplification can arise via an inductive bias in gradient descent methods that results in the overestimation of the... |
253801963 | POWDERWORLD: A PLATFORM FOR UNDERSTANDING GENERALIZATION VIA RICH TASK DISTRIBUTIONS | One of the grand challenges of reinforcement learning is the ability to generalize to new tasks.However, general agents require a set of rich, diverse tasks to train on.Designing a 'foundation environment' for such tasks is tricky -the ideal environment would support a range of emergent phenomena, an expressive task sp... |
249888901 | THE POWER OF REGULARIZATION IN SOLVING EXTENSIVE-FORM GAMES | In this paper, we investigate the power of regularization, a common technique in reinforcement learning and optimization, in solving extensive-form games (EFGs). We propose a series of new algorithms based on regularizing the payoff functions of the game, and establish a set of convergence results that strictly improve... |
231632937 | HIERARCHICAL REINFORCEMENT LEARNING BY DISCOVERING INTRINSIC OPTIONS | We propose a hierarchical reinforcement learning method, HIDIO, that can learn task-agnostic options in a self-supervised manner while jointly learning to utilize them to solve sparse-reward tasks. Unlike current hierarchical RL approaches that tend to formulate goal-reaching low-level tasks or pre-define ad hoc lowerl... |
246904522 | REVISITING OVER-SMOOTHING IN BERT FROM THE PERSPECTIVE OF GRAPH | Recently over-smoothing phenomenon of Transformer-based models is observed in both vision and language fields. However, no existing work has delved deeper to further investigate the main cause of this phenomenon. In this work, we make the attempt to analyze the over-smoothing problem from the perspective of graph, wher... |
252846609 | Few-shot Backdoor Attacks via Neural Tangent Kernels | In a backdoor attack, an attacker injects corrupted examples into the training set. The goal of the attacker is to cause the final trained model to predict the attacker's desired target label when a predefined trigger is added to test inputs. Central to these attacks is the trade-off between the success rate of the att... |
257834209 | SEMI-PARAMETRIC INDUCING POINT NETWORKS AND NEURAL PROCESSES | We introduce semi-parametric inducing point networks (SPIN), a general-purpose architecture that can query the training set at inference time in a compute-efficient manner. Semi-parametric architectures are typically more compact than parametric models, but their computational complexity is often quadratic. In contrast... |
52980218 | EFFICIENT AUGMENTATION VIA DATA SUBSAMPLING | Data augmentation is commonly used to encode invariances in learning methods. However, this process is often performed in an inefficient manner, as artificial examples are created by applying a number of transformations to all points in the training set. The resulting explosion of the dataset size can be an issue in te... |
232257804 | IMPLICIT NORMALIZING FLOWS | |
263831863 | SELF-SUPERVISED DATASET DISTILLATION FOR TRANSFER LEARNING | Dataset distillation methods have achieved remarkable success in distilling a large dataset into a small set of representative samples.However, they are not designed to produce a distilled dataset that can be effectively used for facilitating selfsupervised pre-training.To this end, we propose a novel problem of distil... |
5763832 | A Differentiable Physics Engine for Deep Learning in Robotics | An important field in robotics is the optimization of controllers. Currently, robots are often treated as a black box in this optimization process, which is the reason why derivative-free optimization methods such as evolutionary algorithms or reinforcement learning are omnipresent. When gradient-based methods are used... |
261245530 | INSERTNERF: INSTILLING GENERALIZABILITY INTO NERF WITH HYPERNET MODULES | Generalizing Neural Radiance Fields (NeRF) to new scenes is a significant challenge that existing approaches struggle to address without extensive modifications to vanilla NeRF framework. We introduce InsertNeRF, a method for INStilling gEneRalizabiliTy into NeRF. By utilizing multiple plug-and-play HyperNet modules, I... |
21850704 | A Deep Reinforced Model for Abstractive Summarization | Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good performance on short input and output sequences. However, for longer documents and summaries, these models often include repetitive and incoherent phrases. We introduce a neural network model with intra-attention and a new tr... |
239009555 | ON-POLICY MODEL ERRORS IN REINFORCEMENT LEARNING | Model-free reinforcement learning algorithms can compute policy gradients given sampled environment transitions, but require large amounts of data. In contrast, model-based methods can use the learned model to generate new data, but model errors and bias can render learning unstable or suboptimal. In this paper, we pre... |
43939886 | DEEP LEARNING GENERALIZES BECAUSE THE PARAMETER-FUNCTION MAP IS BIASED TOWARDS SIMPLE FUNCTIONS | Deep neural networks generalize remarkably well without explicit regularization even in the strongly over-parametrized regime. This success suggests that some form of implicit regularization must be at work. In this paper we argue that a strong intrinsic bias in the parameter-function map helps explain the success of d... |
7305965 | OPTIMAL BINARY AUTOENCODING WITH PAIRWISE CORRELATIONS | We formulate learning of a binary autoencoder as a biconvex optimization problem which learns from the pairwise correlations between encoded and decoded bits. Among all possible algorithms that use this information, ours finds the autoencoder that reconstructs its inputs with worst-case optimal loss. The optimal decode... |
261697392 | InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation | Diffusion models have revolutionized text-to-image generation with its exceptional quality and creativity. However, its multi-step sampling process is known to be slow, often requiring tens of inference steps to obtain satisfactory results. Previous attempts to improve its sampling speed and reduce computational costs ... |
3536139 | EMERGENCE OF GRID-LIKE REPRESENTATIONS BY TRAINING RECURRENT NEURAL NETWORKS TO PERFORM SPATIAL LOCALIZATION | Decades of research on the neural code underlying spatial navigation have revealed a diverse set of neural response properties. The Entorhinal Cortex (EC) of the mammalian brain contains a rich set of spatial correlates, including grid cells which encode space using tessellating patterns. However, the mechanisms and fu... |
220302524 | Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval | Conducting text retrieval in a dense learned representation space has many intriguing advantages over sparse retrieval. Yet the effectiveness of dense retrieval (DR) often requires combination with sparse retrieval. In this paper, we identify that the main bottleneck is in the training mechanisms, where the negative in... |
252596001 | COMPOSITIONAL SEMANTIC PARSING WITH LARGE LANGUAGE MODELS | Humans can reason compositionally when presented with new tasks. Previous research shows that appropriate prompting techniques enable large language models (LLMs) to solve artificial compositional generalization tasks such as SCAN.In this work, we identify additional challenges in more realistic semantic parsing tasks ... |
259095643 | On the Reliability of Watermarks for Large Language Models | As LLMs become commonplace, machine-generated text has the potential to flood the internet with spam, social media bots, and valueless content. Watermarking is a simple and effective strategy for mitigating such harms by enabling the detection and documentation of LLM-generated text. Yet a crucial question remains: How... |
247595088 | HALF-INVERSE GRADIENTS FOR PHYSICAL DEEP LEARNING | Recent works in deep learning have shown that integrating differentiable physics simulators into the training process can greatly improve the quality of results. Although this combination represents a more complex optimization task than supervised neural network training, the same gradient-based optimizers are typicall... |
259342096 | Mixture-of-Experts Meets Instruction Tuning: A Winning Combination for Large Language Models | Sparse Mixture-of-Experts (MoE) is a neural architecture design that can be utilized to add learnable parameters to Large Language Models (LLMs) without increasing inference cost. Instruction tuning is a technique for training LLMs to follow instructions. We advocate combining these two approaches, as we find that MoE ... |
21196492 | DCN+: MIXED OBJECTIVE AND DEEP RESIDUAL COATTENTION FOR QUESTION ANSWERING | Traditional models for question answering optimize using cross entropy loss, which encourages exact answers at the cost of penalizing nearby or overlapping answers that are sometimes equally accurate. We propose a mixed objective that combines cross entropy loss with self-critical policy learning. The objective uses re... |
247446857 | OPTIMIZER AMALGAMATION | Selecting an appropriate optimizer for a given problem is of major interest for researchers and practitioners. Many analytical optimizers have been proposed using a variety of theoretical and empirical approaches; however, none can offer a universal advantage over other competitive optimizers. We are thus motivated to ... |
254926490 | TASK AMBIGUITY IN HUMANS AND LANGUAGE MODELS | Language models have recently achieved strong performance across a wide range of NLP benchmarks. However, unlike benchmarks, real world tasks are often poorly specified, and agents must deduce the user's intended behavior from a combination of context, instructions, and examples. We investigate how both humans and mode... |
52912260 | LEARNING TO PROPAGATE LABELS: TRANSDUCTIVE PROPAGATION NETWORK FOR FEW-SHOT LEARNING | The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class.The recently introduced meta-learning approaches tackle this problem by learning a generic classifier across a large number of multiclass classification tasks and generali... |
3535369 | BACKPROPAGATION THROUGH THE VOID: OPTIMIZING CONTROL VARIATES FOR BLACK-BOX GRADIENT ESTIMATION | Gradient-based optimization is the foundation of deep learning and reinforcement learning. Even when the mechanism being optimized is unknown or not differentiable, optimization using high-variance or biased gradient estimates is still often the best strategy. We introduce a general framework for learning low-variance,... |
263834989 | BEYOND MEMORIZATION: VIOLATING PRIVACY VIA INFERENCE WITH LARGE LANGUAGE MODELS | Current privacy research on large language models (LLMs) primarily focuses on the issue of extracting memorized training data. At the same time, models' inference capabilities have increased drastically. This raises the key question of whether current LLMs could violate individuals' privacy by inferring personal attrib... |
247595243 | DO DEEP NETWORKS TRANSFER INVARIANCES ACROSS CLASSES? | To generalize well, classifiers must learn to be invariant to nuisance transformations that do not alter an input's class. Many problems have "class-agnostic" nuisance transformations that apply similarly to all classes, such as lighting and background changes for image classification. Neural networks can learn these i... |
252683543 | A NON-MONOTONIC SELF-TERMINATING LANGUAGE MODEL | Recent large-scale neural autoregressive sequence models have shown impressive performances on a variety of natural language generation tasks. However, their generated sequences often exhibit degenerate properties such as non-termination, undesirable repetition, and premature termination, when generated with decoding a... |
246240237 | IMPLICIT BIAS OF PROJECTED SUBGRADIENT METHOD GIVES PROVABLE ROBUST RECOVERY OF SUBSPACES OF UNKNOWN CODIMENSION | Robust subspace recovery (RSR) is a fundamental problem in robust representation learning. Here we focus on a recently proposed RSR method termed Dual Principal Component Pursuit (DPCP) approach, which aims to recover a basis of the orthogonal complement of the subspace and is amenable to handling subspaces of high rel... |
263671656 | FREEREG: IMAGE-TO-POINT CLOUD REGISTRATION LEVERAGING PRETRAINED DIFFUSION MODELS AND MONOCULAR DEPTH ESTIMATORS | Matching cross-modality features between images and point clouds is a fundamental problem for image-to-point cloud registration.However, due to the modality difference between images and points, it is difficult to learn robust and discriminative cross-modality features by existing metric learning methods for feature ma... |
202660778 | SAMPLE EFFICIENT POLICY GRADIENT METHODS WITH RECURSIVE VARIANCE REDUCTION | Improving the sample efficiency in reinforcement learning has been a longstanding research problem. In this work, we aim to reduce the sample complexity of existing policy gradient methods. We propose a novel policy gradient algorithm called SRVR-PG, which only requires O(1/ 3/2 ) 1 episodes to find anapproximate stati... |
235293695 | ONLINE CORESET SELECTION FOR REHEARSAL-BASED CONTINUAL LEARNING | A dataset is a shred of crucial evidence to describe a task. However, each data point in the dataset does not have the same potential, as some of the data points can be more representative or informative than others. This unequal importance among the data points may have a large impact in rehearsal-based continual lear... |
233378598 | UNDISTILLABLE: MAKING A NASTY TEACHER THAT CANNOT TEACH STUDENTS | Knowledge Distillation (KD) is a widely used technique to transfer knowledge from pre-trained teacher models to (usually more lightweight) student models. However, in certain situations, this technique is more of a curse than a blessing. For instance, KD poses a potential risk of exposing intellectual properties (IPs):... |
238582772 | GRAPH-GUIDED NETWORK FOR IRREGULARLY SAMPLED MULTIVARIATE TIME SERIES | In many domains, including healthcare, biology, and climate science, time series are irregularly sampled with varying time intervals between successive readouts and different subsets of variables (sensors) observed at different time points. Here, we introduce RAINDROP, a graph neural network that embeds irregularly sam... |
253116642 | MULTI-LINGUAL EVALUATION OF CODE GENERATION MODELS | We present new benchmarks for evaluating code generation models: MBXP, Multilingual HumanEval, and MathQA-X. These datasets encompass over 10 programming languages and are generated using a scalable conversion framework that transpiles prompts and test cases from the original Python datasets into the corresponding data... |
252668582 | SPARSITY-CONSTRAINED OPTIMAL TRANSPORT | Regularized optimal transport (OT) is now increasingly used as a loss or as a matching layer in neural networks. Entropy-regularized OT can be computed using the Sinkhorn algorithm but it leads to fully-dense transportation plans, meaning that all sources are (fractionally) matched with all targets. To address this iss... |
220302148 | Tilted Empirical Risk Minimization | Empirical risk minimization (ERM) is typically designed to perform well on the average loss, which can result in estimators that are sensitive to outliers, generalize poorly, or treat subgroups unfairly. While many methods aim to address these problems individually, in this work, we explore them through a unified frame... |
245828046 | QUANTITATIVE PERFORMANCE ASSESSMENT OF CNN UNITS VIA TOPOLOGICAL ENTROPY CALCULATION | Identifying the status of individual network units is critical for understanding the mechanism of convolutional neural networks (CNNs). However, it is still challenging to reliably give a general indication of unit status, especially for units in different network models. To this end, we propose a novel method for quan... |
211132990 | BATCHENSEMBLE: AN ALTERNATIVE APPROACH TO EFFICIENT ENSEMBLE AND LIFELONG LEARNING | Ensembles, where multiple neural networks are trained individually and their predictions are averaged, have been shown to be widely successful for improving both the accuracy and predictive uncertainty of single neural networks. However, an ensemble's cost for both training and testing increases linearly with the numbe... |
213938729 | EDITABLE NEURAL NETWORKS | These days deep neural networks are ubiquitously used in a wide range of tasks, from image classification and machine translation to face identification and selfdriving cars. In many applications, a single model error can lead to devastating financial, reputational and even life-threatening consequences. Therefore, it ... |
259952484 | Quasi-optimal Reinforcement Learning with Continuous Actions | Many real-world applications of reinforcement learning (RL) require making decisions in continuous action environments. In particular, determining the optimal dose level plays a vital role in developing medical treatment regimes. One challenge in adapting existing RL algorithms to medical applications, however, is that... |
259375870 | Teaching Arithmetic to Small Transformers | Large language models like GPT-4 exhibit emergent capabilities across generalpurpose tasks, such as basic arithmetic, when trained on extensive text data, even though these tasks are not explicitly encoded by the unsupervised, next-token prediction objective. This study investigates how small transformers, trained from... |
222141728 | A UNIFYING VIEW ON IMPLICIT BIAS IN TRAINING LINEAR NEURAL NETWORKS | We study the implicit bias of gradient flow (i.e., gradient descent with infinitesimal step size) on linear neural network training. We propose a tensor formulation of neural networks that includes fully-connected, diagonal, and convolutional networks as special cases, and investigate the linear version of the formulat... |
252668746 | A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning | With the increasing need for handling large state and action spaces, general function approximation has become a key technique in reinforcement learning (RL). In this paper, we propose a general framework that unifies model-based and model-free RL, and an Admissible Bellman Characterization (ABC) class that subsumes ne... |
247476364 | Interactive Portrait Harmonization | Current image harmonization methods consider the entire background as the guidance for harmonization. However, this may limit the capability for user to choose any specific object/person in the background to guide the harmonization. To enable flexible interaction between user and harmonization, we introduce interactive... |
253080406 | COMPOSING ENSEMBLES OF PRE-TRAINED MODELS VIA ITERATIVE CONSENSUS | Large pre-trained models exhibit distinct and complementary capabilities dependent on the data they are trained on. Language models such as GPT-3 are capable of textual reasoning but cannot understand visual information, while vision models such as DALL-E can generate photorealistic photos but fail to understand comple... |
259833441 | Plugin estimators for selective classification with out-of-distribution detection | Real-world classifiers can benefit from the option of abstaining from predicting on samples where they have low confidence. Such abstention is particularly useful on samples which are close to the learned decision boundary, or which are outliers with respect to the training sample. These settings have been the subject ... |
238583049 | LEARNING A SUBSPACE OF POLICIES FOR ONLINE ADAPTATION IN REINFORCEMENT LEARNING | Deep Reinforcement Learning (RL) is mainly studied in a setting where the training and the testing environments are similar. But in many practical applications, these environments may differ. For instance, in control systems, the robot(s) on which a policy is learned might differ from the robot(s) on which a policy wil... |
252439127 | Mega: Moving Average Equipped Gated Attention | The design choices in the Transformer attention mechanism, including weak inductive bias and quadratic computational complexity, have limited its application for modeling long sequences.In this paper, we introduce Mega, a simple, theoretically grounded, single-head gated attention mechanism equipped with (exponential) ... |
173991084 | LEARNING TO SOLVE THE CREDIT ASSIGNMENT PROBLEM | Backpropagation is driving today's artificial neural networks (ANNs). However, despite extensive research, it remains unclear if the brain implements this algorithm. Among neuroscientists, reinforcement learning (RL) algorithms are often seen as a realistic alternative: neurons can randomly introduce change, and use un... |
232075892 | ADASPEECH: ADAPTIVE TEXT TO SPEECH FOR CUSTOM VOICE | Custom voice, a specific text to speech (TTS) service in commercial speech platforms, aims to adapt a source TTS model to synthesize personal voice for a target speaker using few speech from her/him. Custom voice presents two unique challenges for TTS adaptation: 1) to support diverse customers, the adaptation model ne... |
213529244 | MODEL-AUGMENTED ACTOR-CRITIC: BACKPROPAGATING THROUGH PATHS | Current model-based reinforcement learning approaches use the model simply as a learned black-box simulator to augment the data for policy optimization or value function learning. In this paper, we show how to make more effective use of the model by exploiting its differentiability. We construct a policy optimization a... |
231918471 | SCALABLE BAYESIAN INVERSE REINFORCEMENT LEARNING | Bayesian inference over the reward presents an ideal solution to the ill-posed nature of the inverse reinforcement learning problem. Unfortunately current methods generally do not scale well beyond the small tabular setting due to the need for an inner-loop MDP solver, and even non-Bayesian methods that do themselves s... |
7942973 | DEEP BIAFFINE ATTENTION FOR NEURAL DEPENDENCY PARSING | This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser. We use a larger but more thoroughly regularized parser than other recent BiLSTM-based approaches, with biaffine classifiers to predict arcs and labels. Our parser gets state of the art ... |
1684853 | EPOPT: LEARNING ROBUST NEURAL NETWORK POLICIES USING MODEL ENSEMBLES | Sample complexity and safety are major challenges when learning policies with reinforcement learning for real-world tasks, especially when the policies are represented using rich function approximators like deep neural networks. Model-based methods where the real-world target domain is approximated using a simulated so... |
250451381 | TEMPORAL DISENTANGLEMENT OF REPRESENTATIONS FOR IMPROVED GENERALISATION IN REINFORCEMENT LEARNING | Reinforcement Learning (RL) agents are often unable to generalise well to environment variations in the state space that were not observed during training. This issue is especially problematic for image-based RL, where a change in just one variable, such as the background colour, can change many pixels in the image. Th... |
254044220 | OFFLINE Q-LEARNING ON DIVERSE MULTI-TASK DATA BOTH SCALES AND GENERALIZES | The potential of offline reinforcement learning (RL) is that high-capacity models trained on large, heterogeneous datasets can lead to agents that generalize broadly, analogously to similar advances in vision and NLP. However, recent works argue that offline RL methods encounter unique challenges to scaling up model ca... |
246996534 | WHEN, WHY, AND WHICH PRETRAINED GANS ARE USEFUL? | The literature has proposed several methods to finetune pretrained GANs on new datasets, which typically results in higher performance compared to training from scratch, especially in the limited-data regime. However, despite the apparent empirical benefits of GAN pretraining, its inner mechanisms were not analyzed ind... |
53114258 | DEEP IMITATIVE MODELS FOR FLEXIBLE INFERENCE, PLANNING, AND CONTROL | Imitation learning provides an appealing framework for autonomous control: in many tasks, demonstrations of preferred behavior can be readily obtained from human experts, removing the need for costly and potentially dangerous online data collection in the real world. However, policies learned with imitation learning ha... |
247411320 | DEEP AUTOAUGMENT | While recent automated data augmentation methods lead to state-of-the-art results, their design spaces and the derived data augmentation strategies still incorporate strong human priors. In this work, instead of fixing a set of hand-picked default augmentations alongside the searched data augmentations, we propose a fu... |
252682980 | Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient | Offline reinforcement learning, which aims at optimizing sequential decision-making strategies with historical data, has been extensively applied in real-life applications. State-Of-The-Art algorithms usually leverage powerful function approximators (e.g. neural networks) to alleviate the sample complexity hurdle for b... |
235358397 | CHURN REDUCTION VIA DISTILLATION | In real-world systems, models are frequently updated as more data becomes available, and in addition to achieving high accuracy, the goal is to also maintain a low difference in predictions compared to the base model (i.e. predictive "churn"). If model retraining results in vastly different behavior, then it could caus... |
249210151 | NEURAL OPTIMAL TRANSPORT WITH GENERAL COST FUNCTIONALS | We introduce a novel neural network-based algorithm to compute optimal transport (OT) plans for general cost functionals.In contrast to common Euclidean costs, i.e., ℓ 1 or ℓ 2 , such functionals provide more flexibility and allow using auxiliary information, such as class labels, to construct the required transport ma... |
252668614 | OUT-OF-DISTRIBUTION DETECTION AND SELECTIVE GENERATION FOR CONDITIONAL LANGUAGE MODELS | Machine learning algorithms typically assume independent and identically distributed samples in training and at test time. Much work has shown that highperforming ML classifiers can degrade significantly and provide overly-confident, wrong classification predictions, particularly for out-of-distribution (OOD) inputs. C... |
21946795 | ENSEMBLE ADVERSARIAL TRAINING: ATTACKS AND DEFENSES | Adversarial examples are perturbed inputs designed to fool machine learning models. Adversarial training injects such examples into training data to increase robustness. To scale this technique to large datasets, perturbations are crafted using fast single-step methods that maximize a linear approximation of the model'... |
219636462 | CPR: Classifier-Projection Regularization for Continual Learning | We propose a general, yet simple patch that can be applied to existing regularizationbased continual learning methods called classifier-projection regularization (CPR). Inspired by both recent results on neural networks with wide local minima and information theory, CPR adds an additional regularization term that maxim... |
222141662 | USABLE INFORMATION AND EVOLUTION OF OPTIMAL REPRESENTATIONS DURING TRAINING | We introduce a notion of usable information contained in the representation learned by a deep network, and use it to study how optimal representations for the task emerge during training, and how they adapt to different tasks. We use this to characterize the transient dynamics of deep neural networks on perceptual deci... |
252907593 | CONTRASTIVE AUDIO-VISUAL MASKED AUTOENCODER | In this paper, we first extend the recent Masked Auto-Encoder (MAE) model from a single modality to audio-visual multi-modalities. Subsequently, we propose the Contrastive Audio-Visual Masked Auto-Encoder (CAV-MAE) by combining contrastive learning and masked data modeling, two major self-supervised learning frameworks... |
203593909 | REVISITING SELF-TRAINING FOR NEURAL SEQUENCE GENERATION | Self-training is one of the earliest and simplest semi-supervised methods. The key idea is to augment the original labeled dataset with unlabeled data paired with the model's prediction (i.e. the pseudo-parallel data). While self-training has been extensively studied on classification problems, in complex sequence gene... |
219708742 | Minimum Width for Universal Approximation | The universal approximation property of width-bounded networks has been studied as a dual of classical universal approximation results on depth-bounded networks.However, the critical width enabling the universal approximation has not been exactly characterized in terms of the input dimension d x and the output dimensio... |
220768638 | Unsupervised Discovery of 3D Physical Objects from Video | We study the problem of unsupervised physical object discovery. Unlike existing frameworks that aim to learn to decompose scenes into 2D segments purely based on each object's appearance, we explore how physics, especially object interactions, facilitates learning to disentangle and segment instances from raw videos, a... |
84186721 | A GENERATIVE MODEL FOR ELECTRON PATHS | Chemical reactions can be described as the stepwise redistribution of electrons in molecules. As such, reactions are often depicted using "arrow-pushing" diagrams which show this movement as a sequence of arrows. We propose an electron path prediction model (ELECTRO) to learn these sequences directly from raw reaction ... |
254535921 | CONFIDENCE-CONDITIONED VALUE FUNCTIONS FOR OFFLINE REINFORCEMENT LEARNING | Offline reinforcement learning (RL) promises the ability to learn effective policies solely using existing, static datasets, without any costly online interaction.To do so, offline RL methods must handle distributional shift between the dataset and the learned policy.The most common approach is to learn conservative, o... |
203591409 | IDENTIFYING THROUGH FLOWS FOR RECOVERING LATENT REPRESENTATIONS | Identifiability, or recovery of the true latent representations from which the observed data originates, is a fundamental goal of representation learning. However, most deep generative models do not address the question of identifiability, and cannot recover the true latent sources that generate the observations. Recen... |
1880070 | Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses | Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and correlate very poorly with human judgements of response quality. Yet having an accurate automatic evaluation procedure is crucial for dialogue... |
263829563 | OPENWEBMATH: AN OPEN DATASET OF HIGH-QUALITY MATHEMATICAL WEB TEXT | There is growing evidence that pretraining on high quality, carefully thought-out tokens such as code or mathematics plays an important role in improving the reasoning abilities of large language models.For example, Minerva, a PaLM model finetuned on billions of tokens of mathematical documents from arXiv and the web, ... |
252762187 | UNDERSTANDING THE COVARIANCE STRUCTURE OF CONVOLUTIONAL FILTERS | Neural network weights are typically initialized at random from univariate distributions, controlling just the variance of individual weights even in highlystructured operations like convolutions. Recent ViT-inspired convolutional networks such as ConvMixer and ConvNeXt use large-kernel depthwise convolutions whose lea... |
End of preview. Expand in Data Studio
LitSearch (princeton-nlp/LitSearch) in the unified retrieval schema: 597 queries, 64183 documents (title + abstract), 639 qrels.
- Downloads last month
- 21