_id stringlengths 4 10 | text stringlengths 0 18.4k | title stringlengths 0 8.56k |
|---|---|---|
d236134152 | We present DrQ-v2, a model-free reinforcement learning (RL) algorithm for visual continuous control. DrQ-v2 builds on DrQ, an off-policy actor-critic approach that uses data augmentation to learn directly from pixels. We introduce several improvements that yield state-of-the-art results on the DeepMind Control Suite. N... | Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning |
d16138044 | The loss surface of deep neural networks has recently attracted interest in the optimization and machine learning communities as a prime example of high-dimensional non-convex problem. Some insights were recently gained using spin glass models and mean-field approximations, but at the expense of strongly simplifying th... | Topology and Geometry of Half-Rectified Network Optimization |
d239049858 | Machine learning training methods depend plentifully and intricately on hyperparameters, motivating automated strategies for their optimisation. Many existing algorithms restart training for each new hyperparameter choice, at considerable computational cost. Some hypergradient-based one-pass methods exist, but these ei... | SCALABLE ONE-PASS OPTIMISATION OF HIGH-DIMENSIONAL WEIGHT-UPDATE HYPERPARAMETERS BY IMPLICIT DIFFERENTIATION |
d229924317 | A key aspect of human intelligence is the ability to infer abstract rules directly from high-dimensional sensory data, and to do so given only a limited amount of training experience. Deep neural network algorithms have proven to be a powerful tool for learning directly from high-dimensional data, but currently lack th... | EMERGENT SYMBOLS THROUGH BINDING IN EXTERNAL MEMORY |
d211549689 | Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence. Recently, classification-based methods were shown to achieve superior results on this task. In this work, we present a unifying view and propose an open-set method, G... | CLASSIFICATION-BASED ANOMALY DETECTION FOR GENERAL DATA |
d3707461 | We interpret meta-reinforcement learning as the problem of learning how to quickly find a good sampling distribution in a new environment. This interpretation leads to the development of two new meta-reinforcement learning algorithms: E-MAML and E-RL 2 . Results are presented on a new environment we call 'Krazy World':... | Some Considerations on Learning to Explore via Meta-Reinforcement Learning |
d49299039 | We present a new latent model of natural images that can be learned on large-scale datasets. The learning process provides a latent embedding for every image in the training dataset, as well as a deep convolutional network that maps the latent space to the image space. After training, the new model provides a strong an... | LATENT CONVOLUTIONAL MODELS |
d58006691 | End-to-end task-oriented dialogue is challenging since knowledge bases are usually large, dynamic and hard to incorporate into a learning framework. We propose the global-to-local memory pointer (GLMP) networks to address this issue. In our model, a global memory encoder and a local memory decoder are proposed to share... | GLOBAL-TO-LOCAL MEMORY POINTER NETWORKS FOR TASK-ORIENTED DIALOGUE |
d210064473 | The ability to decompose complex multi-object scenes into meaningful abstractions like objects is fundamental to achieve higher-level cognition. Previous approaches for unsupervised object-oriented scene representation learning are either based on spatial-attention or scene-mixture approaches and limited in scalability... | SPACE: UNSUPERVISED OBJECT-ORIENTED SCENE REPRESENTATION VIA SPATIAL ATTENTION AND DECOMPOSITION |
d260125817 | This paper studies the problem of training a two-layer ReLU network for binary classification using gradient flow with small initialization. We consider a training dataset with well-separated input vectors: Any pair of input data with the same label are positively correlated, and any pair with different labels are nega... | Early Neuron Alignment in Two-layer ReLU Networks with Small Initialization |
d260154786 | In reinforcement learning (RL), rewards of states are typically considered additive, and following the Markov assumption, they are independent of states visited previously. In many important applications, such as coverage control, experiment design and informative path planning, rewards naturally have diminishing retur... | Submodular Reinforcement Learning |
d257557548 | Most bandit algorithms assume that the reward variances or their upper bounds are known, and that they are the same for all arms.This naturally leads to suboptimal performance and higher regret due to variance overestimation.On the other hand, underestimated reward variances may lead to linear regret due to committing ... | Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling |
d254591453 | We present the Group Propagation Vision Transformer (GPViT): a novel nonhierarchical (i.e. non-pyramidal) transformer model designed for general visual recognition with high-resolution features. High-resolution features (or tokens) are a natural fit for tasks that involve perceiving fine-grained details such as detecti... | GPVIT: A HIGH RESOLUTION NON-HIERARCHICAL VISION TRANSFORMER WITH GROUP PROPAGATION |
d233169183 | Simulated virtual environments serve as one of the main driving forces behind developing and evaluating skill learning algorithms. However, existing environments typically only simulate rigid body physics. Additionally, the simulation process usually does not provide gradients that might be useful for planning and cont... | PLASTICINELAB: A SOFT-BODY MANIPULATION BENCHMARK WITH DIFFERENTIABLE PHYSICS |
d238407985 | Pre-trained Natural Language Processing (NLP) models can be easily adapted to a variety of downstream language tasks. This significantly accelerates the development of language models. However, NLP models have been shown to be vulnerable to backdoor attacks, where a pre-defined trigger word in the input text causes mod... | BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models |
d252735209 | In cooperative multi-agent reinforcement learning, a team of agents works together to achieve a common goal.Different environments or tasks may require varying degrees of coordination among agents in order to achieve the goal in an optimal way.The nature of coordination will depend on the properties of the environmenti... | STATEFUL ACTIVE FACILITATOR: COORDINATION AND ENVIRONMENTAL HETEROGENEITY IN COOPERATIVE MULTI-AGENT REINFORCEMENT LEARNING |
d238353834 | This work addresses one-shot set and graph generation, and, more specifically, the parametrization of probabilistic decoders that map a vector-shaped prior to a distribution over sets or graphs. Sets and graphs are most commonly generated by first sampling points i.i.d. from a normal distribution, and then processing t... | TOP-N: EQUIVARIANT SET AND GRAPH GENERATION WITHOUT EXCHANGEABILITY |
d211069110 | We propose procedures for evaluating and strengthening contextual embedding alignment and show that they are useful in analyzing and improving multilingual BERT. In particular, after our proposed alignment procedure, BERT exhibits significantly improved zero-shot performance on XNLI compared to the base model, remarkab... | MULTILINGUAL ALIGNMENT OF CONTEXTUAL WORD REPRESENTATIONS |
d264439306 | We study deceptive fairness attacks on graphs to answer the following question: How can we achieve poisoning attacks on a graph learning model to exacerbate the bias deceptively?We answer this question via a bi-level optimization problem and propose a meta learning-based framework named FATE.FATE is broadly applicable ... | DECEPTIVE FAIRNESS ATTACKS ON GRAPHS VIA META LEARNING |
d235266159 | In learning with noisy labels, the sample selection approach is very popular, which regards small-loss data as correctly labeled during training. However, losses are generated on-the-y based on the model being trained with noisy labels, and thus large-loss data are likely but not certainly to be incorrect. There are ac... | Sample Selection with Uncertainty of Losses for Learning with Noisy Labels |
d263829872 | 3D visual grounding is the ability to localize objects in 3D scenes conditioned by utterances. Most existing methods devote the referring head to localize the referred object directly, causing failure in complex scenarios. In addition, it does not illustrate how and why the network reaches the final decision. In this p... | COT3DREF: CHAIN-OF-THOUGHTS DATA-EFFICIENT 3D VISUAL GROUNDING |
d216562627 | We propose a simple data augmentation technique that can be applied to standard model-free reinforcement learning algorithms, enabling robust learning directly from pixels without the need for auxiliary losses or pre-training. The approach leverages input perturbations commonly used in computer vision tasks to regulari... | Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels |
d257353706 | Unsupervised pretraining, which learns a useful representation using a large amount of unlabeled data to facilitate the learning of downstream tasks, is a critical component of modern large-scale machine learning systems. Despite its tremendous empirical success, the rigorous theoretical understanding of why unsupervis... | On the Provable Advantage of Unsupervised Pretraining |
d263334587 | Temporal abstraction and efficient planning pose significant challenges in offline reinforcement learning, mainly when dealing with domains that involve temporally extended tasks and delayed sparse rewards.Existing methods typically plan in the raw action space and can be inefficient and inflexible.Latent action spaces... | Efficient Planning with Latent Diffusion |
d232240392 | How to improve generative modeling by better exploiting spatial regularities and coherence in images? We introduce a novel neural network for building image generators (decoders) and apply it to variational autoencoders (VAEs). In our spatial dependency networks (SDNs), feature maps at each level of a deep neural net a... | SPATIAL DEPENDENCY NETWORKS: NEURAL LAYERS FOR IMPROVED GENERATIVE IMAGE MODELING |
d253224118 | Comparing learned neural representations in neural networks is a challenging but important problem, which has been approached in different ways. The Centered Kernel Alignment (CKA) similarity metric, particularly its linear variant, has recently become a popular approach and has been widely used to compare representati... | RELIABILITY OF CKA AS A SIMILARITY MEASURE IN DEEP LEARNING |
d250243971 | Factorizing a large matrix into small matrices is a popular strategy for model compression. Singular value decomposition (SVD) plays a vital role in this compression strategy, approximating a learned matrix with fewer parameters. However, SVD minimizes the squared error toward reconstructing the original matrix without... | LANGUAGE MODEL COMPRESSION WITH WEIGHTED LOW-RANK FACTORIZATION |
d264426355 | Large language models (LLMs) have made impressive progress in natural language processing.These models rely on proper human instructions (or prompts) to generate suitable responses.However, the potential of LLMs are not fully harnessed by commonly-used prompting methods: many human-in-the-loop algorithms employ ad-hoc ... | Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing |
d259341801 | Seismic advances in generative AI algorithms for imagery, text, and other data types has led to the temptation to use synthetic data to train next-generation models. Repeating this process creates an autophagous ("self-consuming") loop whose properties are poorly understood. We conduct a thorough analytical and empiric... | Self-Consuming Generative Models Go MAD |
d7067017 | The success of deep learning in numerous application domains created the desire to run and train them on mobile devices. This however, conflicts with their computationally, memory and energy intense nature, leading to a growing interest in compression. Recent work byHan et al. (2015a)propose a pipeline that involves re... | SOFT WEIGHT-SHARING FOR NEURAL NETWORK COMPRESSION |
d242757066 | Automatic Music Transcription (AMT), inferring musical notes from raw audio, is a challenging task at the core of music understanding. Unlike Automatic Speech Recognition (ASR), which typically focuses on the words of a single speaker, AMT often requires transcribing multiple instruments simultaneously, all while prese... | MT3: MULTI-TASK MULTITRACK MUSIC TRANSCRIPTION |
d263909329 | Scaling of deep neural networks, especially Transformers, is pivotal for their surging performance and has further led to the emergence of sophisticated reasoning capabilities in foundation models.Such scaling generally requires training large models from scratch with random initialization, failing to leverage the know... | LEMON: LOSSLESS MODEL EXPANSION |
d264487385 | Generative models have gained more and more attention in recent years for their remarkable success in tasks that required estimating and sampling data distribution to generate high-fidelity synthetic data.In speech, text-to-speech synthesis and neural vocoder are good examples where generative models have shined.While ... | GENERATIVE PRE-TRAINING FOR SPEECH WITH FLOW MATCHING |
d219980606 | Segmented models are widely used to describe non-stationary sequential data with discrete change points. Their estimation usually requires solving a mixed discretecontinuous optimization problem, where the segmentation is the discrete part and all other model parameters are continuous. A number of estimation algorithms... | Differentiable Segmentation of Sequences |
d240354190 | Neural networks in the lazy training regime converge to kernel machines. Can neural networks in the rich feature learning regime learn a kernel machine with a data-dependent kernel? We demonstrate that this can indeed happen due to a phenomenon we term silent alignment, which requires that the tangent kernel of a netwo... | NEURAL NETWORKS AS KERNEL LEARNERS: THE SILENT ALIGNMENT EFFECT |
d249626390 | Neural message passing is a basic feature extraction unit for graph-structured data considering neighboring node features in network propagation from one layer to the next. We model such process by an interacting particle system with attractive and repulsive forces and the Allen-Cahn force arising in the modeling of ph... | ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks |
d264146169 | Long-term time series forecasting (LTSF) is important for various domains but is confronted by challenges in handling the complex temporal-contextual relationships.As multivariate input models underperforming some recent univariate counterparts, we posit that the issue lies in the inefficiency of existing multivariate ... | ARM: REFINING MULTIVARIATE FORECASTING WITH ADAPTIVE TEMPORAL-CONTEXTUAL LEARNING |
d53036488 | The field of few-shot learning has recently seen substantial advancements. Most of these advancements came from casting few-shot learning as a meta-learning problem. Model Agnostic Meta Learning or MAML is currently one of the best approaches for few-shot learning via meta-learning. MAML is simple, elegant and very pow... | HOW TO TRAIN YOUR MAML |
d252716013 | Large language models (LLMs) transfer well to new tasks out-of-the-box simply given a natural language prompt that demonstrates how to perform the task and no additional training. Prompting is a brittle process wherein small modifications to the prompt can cause large variations in the model predictions, and therefore ... | ASK ME ANYTHING: A SIMPLE STRATEGY FOR PROMPTING LANGUAGE MODELS |
d3861760 | Stochastic neural net weights are used in a variety of contexts, including regularization, Bayesian neural nets, exploration in reinforcement learning, and evolution strategies. Unfortunately, due to the large number of weights, all the examples in a mini-batch typically share the same weight perturbation, thereby limi... | FLIPOUT: EFFICIENT PSEUDO-INDEPENDENT WEIGHT PERTURBATIONS ON MINI-BATCHES |
d67855552 | Neural networks are vulnerable to small adversarial perturbations. Existing literature largely focused on understanding and mitigating the vulnerability of learned models. In this paper, we demonstrate an intriguing phenomenon about the most popular robust training method in the literature, adversarial training: Advers... | ON THE SENSITIVITY OF ADVERSARIAL ROBUSTNESS TO INPUT DATA DISTRIBUTIONS |
d212945787 | Text generation is ubiquitous in many NLP tasks, from summarization, to dialogue and machine translation. The dominant parametric approach is based on locally normalized models which predict one word at a time. While these work remarkably well, they are plagued by exposure bias due to the greedy nature of the generatio... | RESIDUAL ENERGY-BASED MODELS FOR TEXT GENERATION |
d252734952 | Publicly available source-code libraries are continuously growing and changing. This makes it impossible for models of code to keep current with all available APIs by simply training these models on existing code repositories. Thus, existing models inherently cannot generalize to using unseen functions and libraries, b... | DocPrompting: GENERATING CODE BY RETRIEVING THE DOCS |
d258866127 | Recent embedding-based methods have achieved great successes on exploiting entity alignment from knowledge graph (KG) embeddings of multiple modals. In this paper, we study embedding-based entity alignment (EEA) from a perspective of generative models. We show that EEA is a special problem where the main objective is a... | Revisit and Outstrip Entity Alignment: A Perspective of Generative Models |
d3398677 | Generative adversarial networks (GANs) are a family of generative models that do not minimize a single training criterion. Unlike other generative models, the data distribution is learned via a game between a generator (the generative model) and a discriminator (a teacher providing training signal) that each minimize t... | Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step |
d250089125 | State space models have shown to be effective at modeling long range dependencies, specially on sequence classification tasks. In this work we focus on autoregressive sequence modeling over English books, Github source code and ArXiv mathematics articles. Based on recent developments around the effectiveness of gated a... | Long Range Language Modeling via Gated State Spaces |
d248965495 | We propose Adaptive Deep Kernel Fitting with Implicit Function Theorem (ADKF-IFT), a novel framework for learning deep kernel Gaussian processes (GPs) by interpolating between meta-learning and conventional deep kernel learning. Our approach employs a bilevel optimization objective where we meta-learn generally useful ... | META-LEARNING ADAPTIVE DEEP KERNEL GAUSSIAN PROCESSES FOR MOLECULAR PROPERTY PREDICTION |
d235670132 | Evading adversarial example detection defenses requires finding adversarial examples that must simultaneously (a) be misclassified by the model and (b) be detected as non-adversarial. We find that existing attacks that attempt to satisfy multiple simultaneous constraints often over-optimize against one constraint at th... | Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent |
d251468096 | Adversarial training is a widely used strategy for making neural networks resistant to adversarial perturbations. For a neural network of width m, n input training data in d dimension, it takes Ω(mnd) time cost per training iteration for the forward and backward computation. In this paper we analyze the convergence gua... | A Sublinear Adversarial Training Algorithm |
d432949 | A framework is presented for unsupervised learning of representations based on infomax principle for large-scale neural populations. We use an asymptotic approximation to the Shannon's mutual information for a large neural population to demonstrate that a good initial approximation to the global information-theoretic o... | AN INFORMATION-THEORETIC FRAMEWORK FOR FAST AND ROBUST UNSUPERVISED LEARNING VIA NEURAL POPULATION INFOMAX |
d220968856 | Learning long-term dynamics models is the key to understanding physical common sense. Most existing approaches on learning dynamics from visual input sidestep long-term predictions by resorting to rapid re-planning with short-term models. This not only requires such models to be super accurate but also limits them only... | Learning Long-term Visual Dynamics with Region Proposal Interaction Networks |
d211678094 | Federated learning is a distributed machine learning paradigm in which a large number of clients coordinate with a central server to learn a model without sharing their own training data. Standard federated optimization methods such as Federated Averaging (FEDAVG) are often difficult to tune and exhibit unfavorable con... | ADAPTIVE FEDERATED OPTIMIZATION |
d52962648 | We show that there exists an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accuracy ... | Robustness May Be at Odds with Accuracy |
d263831484 | The application of machine learning (ML) in a range of geospatial tasks is increasingly common but often relies on globally available covariates such as satellite imagery that can either be expensive or lack predictive power.Here we explore the question of whether the vast amounts of knowledge found in Internet languag... | GEOLLM: EXTRACTING GEOSPATIAL KNOWLEDGE FROM LARGE LANGUAGE MODELS |
d53046534 | A neural network deployed in the wild may be asked to make predictions for inputs that were drawn from a different distribution than that of the training data. A plethora of work has demonstrated that it is easy to find or synthesize inputs for which a neural network is highly confident yet wrong. Generative models are... | DO DEEP GENERATIVE MODELS KNOW WHAT THEY DON'T KNOW? |
d260438511 | Text-to-image generative models can produce photo-realistic images for an extremely broad range of concepts, and their usage has proliferated widely among the general public.Yet, these models have numerous drawbacks, including their potential to generate images featuring sexually explicit content, mirror artistic style... | Circumventing Concept Erasure Methods For Text-to-Image Generative Models |
d258865692 | In this paper, we investigate the problem of offline Preference-based Reinforcement Learning (PbRL) with human feedback where feedback is available in the form of preference between trajectory pairs rather than explicit rewards. Our proposed algorithm consists of two main steps: (1) estimate the implicit reward using M... | Provable Offline Preference-Based Reinforcement Learning |
d53869047 | The Differentiable Neural Computer (DNC) can learn algorithmic and question answering tasks. An analysis of its internal activation patterns reveals three problems: Most importantly, the lack of key-value separation makes the address distribution resulting from content-based look-up noisy and flat, since the value infl... | IMPROVING DIFFERENTIABLE NEURAL COMPUTERS THROUGH MEMORY MASKING, DE-ALLOCATION, AND LINK DISTRIBUTION SHARPNESS CONTROL |
d33985488 | Goal-conditional policies allow reinforcement learning agents to pursue specific goals during different episodes. In addition to their potential to generalize desired behavior to unseen goals, such policies may also help in defining options for arbitrary subgoals, enabling higher-level planning. While trying to achieve... | Hindsight policy gradients |
d56475856 | Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations or unseen situations cause proficient but specialized policies to fail at test time. Given that it is impractical ... | LEARNING TO ADAPT IN DYNAMIC, REAL-WORLD ENVIRONMENTS THROUGH META-REINFORCEMENT LEARNING |
d252668297 | Employing a forward diffusion chain to gradually map the data to a noise distribution, diffusion-based generative models learn how to generate the data by inferring a reverse diffusion chain. However, this approach is slow and costly because it needs many forward and reverse steps. We propose a faster and cheaper appro... | Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders |
d249847970 | Many variants of the Wasserstein distance have been introduced to reduce its original computational burden. In particular the Sliced-Wasserstein distance (SW), which leverages one-dimensional projections for which a closed-form solution of the Wasserstein distance is available, has received a lot of interest. Yet, it i... | SPHERICAL SLICED-WASSERSTEIN |
d208006240 | Zeroth-order optimization is the process of minimizing an objective f (x), given oracle access to evaluations at adaptively chosen inputs x. In this paper, we present two simple yet powerful GradientLess Descent (GLD) algorithms that do not rely on an underlying gradient estimate and are numerically stable. We analyze ... | Gradientless Descent: High-Dimensional Zeroth-Order Optimization |
d208637449 | We introduce "Search with Amortized Value Estimates" (SAVE), an approach for combining model-free Q-learning with model-based Monte-Carlo Tree Search (MCTS). In SAVE, a learned prior over state-action values is used to guide MCTS, which estimates an improved set of state-action values. The new Q-estimates are then used... | COMBINING Q-LEARNING AND SEARCH WITH AMORTIZED VALUE ESTIMATES |
d259836930 | Self-supervised learning has recently gained growing interest in molecular modeling for scientific tasks such as AI-assisted drug discovery.Current studies consider leveraging both 2D and 3D molecular structures for representation learning.However, relying on straightforward alignment strategies that treat each modalit... | MULTIMODAL MOLECULAR PRETRAINING VIA MODALITY BLENDING |
d209475155 | This paper proposes a differentiable linear quadratic Model Predictive Control (MPC) framework for safe imitation learning. The infinite-horizon cost is enforced using a terminal cost function obtained from the discrete-time algebraic Riccati equation (DARE), so that the learned controller can be proven to be stabilizi... | INFINITE-HORIZON DIFFERENTIABLE MODEL PREDICTIVE CONTROL |
d238419458 | Large Language Models (LMs) are known to encode world knowledge in their parameters as they pretrain on a vast amount of web corpus, which is often utilized for performing knowledge-dependent downstream tasks such as question answering, fact-checking, and open dialogue. In real-world scenarios, the world knowledge stor... | TOWARDS CONTINUAL KNOWLEDGE LEARNING OF LANGUAGE MODELS |
d231786683 | Multi-view classification (MVC) generally focuses on improving classification accuracy by using information from different views, typically integrating them into a unified comprehensive representation for downstream tasks. However, it is also crucial to dynamically assess the quality of a view for different samples in ... | TRUSTED MULTI-VIEW CLASSIFICATION |
d210116641 | In this paper, a new population-guided parallel learning scheme is proposed to enhance the performance of off-policy reinforcement learning (RL). In the proposed scheme, multiple identical learners with their own value-functions and policies share a common experience replay buffer, and search a good policy in collabora... | POPULATION-GUIDED PARALLEL POLICY SEARCH FOR REINFORCEMENT LEARNING |
d263605851 | Collecting large amounts of real-world interaction data to train general robotic policies is often prohibitively expensive, thus motivating the use of simulation data. However, existing methods for data generation have generally focused on scenelevel diversity (e.g., object instances and poses) rather than task-level d... | GENSIM: GENERATING ROBOTIC SIMULATION TASKS VIA LARGE LANGUAGE MODELS |
d14337532 | Restart techniques are common in gradient-free optimization to deal with multimodal functions. Partial warm restarts are also gaining popularity in gradientbased optimization to improve the rate of convergence in accelerated gradient schemes to deal with ill-conditioned functions. In this paper, we propose a simple war... | SGDR: STOCHASTIC GRADIENT DESCENT WITH WARM RESTARTS |
d3488815 | Recent work has demonstrated that neural networks are vulnerable to adversarial examples, i.e., inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of ... | Towards Deep Learning Models Resistant to Adversarial Attacks |
d53116049 | Apple (a) (b)Coffee machineFigure 1: Our goal is to use scene priors to improve navigation in unseen scenes and towards novel objects. (a) There is no mug in the field of view of the agent, but the likely location for finding a mug is the cabinet near the coffee machine. (b) The agent has not seen a mango before, but i... | VISUAL SEMANTIC NAVIGATION USING SCENE PRIORS |
d49532099 | We address a learning-to-normalize problem by proposing Switchable Normalization (SN), which learns to select different normalizers for different normalization layers of a deep neural network. SN employs three distinct scopes to compute statistics (means and variances) including a channel, a layer, and a minibatch. SN ... | DIFFERENTIABLE LEARNING-TO-NORMALIZE VIA SWITCHABLE NORMALIZATION |
d209439835 | Experimental reproducibility and replicability are critical topics in machine learning. Authors have often raised concerns about their lack in scientific publications to improve the quality of the field. Recently, the graph representation learning field has attracted the attention of a wide research community, which re... | A FAIR COMPARISON OF GRAPH NEURAL NETWORKS FOR GRAPH CLASSIFICATION |
d252846328 | Tabular data is among the oldest and most ubiquitous forms of data. However, the generation of synthetic samples with the original data's characteristics remains a significant challenge for tabular data. While many generative models from the computer vision domain, such as variational autoencoders or generative adversa... | LANGUAGE MODELS ARE REALISTIC TABULAR DATA GENERATORS |
d247447346 | Double descent' delineates the generalization behaviour of models depending on the regime they belong to: under-or over-parameterized. The current theoretical understanding behind the occurrence of this phenomenon is primarily based on linear and kernel regression models -with informal parallels to neural networks via ... | PHENOMENOLOGY OF DOUBLE DESCENT IN FINITE-WIDTH NEURAL NETWORKS |
d222130583 | In many important graph data processing applications the acquired information includes both node features and observations of the graph topology. Graph neural networks (GNNs) are designed to exploit both sources of evidence but they do not optimally trade-off their utility and integrate them in a manner that is also un... | ADAPTIVE UNIVERSAL GENERALIZED PAGERANK GRAPH NEURAL NETWORK |
d238408272 | Given a pair of partially overlapping source and target images and a keypoint in the source image, the keypoint's correspondent in the target image can be either visible, occluded or outside the field of view. Local feature matching methods are only able to identify the correspondent's location when it is visible, whil... | VISUAL CORRESPONDENCE HALLUCINATION |
d247451154 | We study the p regression problem, which requires finding x ∈ R d that minimizes Ax−b p for a matrix A ∈ R n×d and response vector b ∈ R n . There has been recent interest in developing subsampling methods for this problem that can outperform standard techniques when n is very large. However, all known subsampling appr... | Fast Regression for Structured Inputs |
d264818112 | We study the optimisation problem associated with Gaussian process regression using squared loss.The most common approach to this problem is to apply an exact solver, such as conjugate gradient descent, either directly, or to a reducedorder version of the problem.Recently, driven by successes in deep learning, stochast... | STOCHASTIC GRADIENT DESCENT FOR GAUSSIAN PROCESSES DONE RIGHT |
d210848920 | Recent efforts in Dialogue State Tracking (DST) for task-oriented dialogues have progressed toward open-vocabulary or generation-based approaches where the models can generate slot value candidates from the dialogue history itself. These approaches have shown good performance gain, especially in complicated dialogue do... | NON-AUTOREGRESSIVE DIALOG STATE TRACKING |
d254854614 | Many real-world reinforcement learning tasks require control of complex dynamical systems that involve both costly data acquisition processes and large state spaces. In cases where the transition dynamics can be readily evaluated at specified states (e.g., via a simulator), agents can operate in what is often referred ... | NEAR-OPTIMAL POLICY IDENTIFICATION IN ACTIVE REINFORCEMENT LEARNING |
d221397331 | In this paper, we investigate the principle that good explanations are hard to vary in the context of deep learning.We show that averaging gradients across examples -akin to a logical OR (∨) of patterns -can favor memorization and 'patchwork' solutions that sew together different strategies, instead of identifying inva... | Learning explanations that are hard to vary |
d67856290 | We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as offensive basketball gameplay. When modeling such settings, it is often beneficial to design hierarchical models that can capture long-term coordination using intermediate variables. Furtherm... | GENERATING MULTI-AGENT TRAJECTORIES USING PROGRAMMATIC WEAK SUPERVISION |
d247446906 | Automated seizure detection and classification from electroencephalography (EEG) can greatly improve seizure diagnosis and treatment. However, several modeling challenges remain unaddressed in prior automated seizure detection and classification studies: (1) representing non-Euclidean data structure in EEGs, (2) accura... | SELF-SUPERVISED GRAPH NEURAL NETWORKS FOR IMPROVED ELECTROENCEPHALOGRAPHIC SEIZURE ANALYSIS |
d238583252 | Deep convolutional classifiers linearly separate image classes and improve accuracy as depth increases. They progressively reduce the spatial dimension whereas the number of channels grows with depth. Spatial variability is therefore transformed into variability along channels. A fundamental challenge is to understand ... | PHASE COLLAPSE IN NEURAL NETWORKS |
d247315571 | The execution behavior of a program often depends on external resources, such as program inputs or file contents, and so cannot be run in isolation. Nevertheless, software developers benefit from fast iteration loops where automated tools identify errors as early as possible, even before programs can be compiled and ru... | STATIC PREDICTION OF RUNTIME ERRORS BY LEARNING TO EXECUTE PROGRAMS WITH EXTERNAL RESOURCE DESCRIPTIONS |
d264172865 | Metal-organic frameworks (MOFs) are of immense interest in applications such as gas storage and carbon capture due to their exceptional porosity and tunable chemistry.Their modular nature has enabled the use of template-based methods to generate hypothetical MOFs by combining molecular building blocks in accordance wit... | MOFDIFF: COARSE-GRAINED DIFFUSION FOR METAL-ORGANIC FRAMEWORK DESIGN |
d264802494 | Offline reinforcement learning (RL) aims to find a near-optimal policy using pre-collected datasets.In real-world scenarios, data collection could be costly and risky; therefore, offline RL becomes particularly challenging when the indomain data is limited.Given recent advances in Large Language Models (LLMs) and their... | UNLEASHING THE POWER OF PRE-TRAINED LANGUAGE MODELS FOR OFFLINE REINFORCEMENT LEARNING |
d8020289 | Convolutional neural networks (CNNs) are equivariant with respect to translation; a translation in the input causes a translation in the output. Attempts to generalize equivariance have concentrated on rotations. In this paper, we combine the idea of the spatial transformer, and the canonical coordinate representations... | Polar Transformer Networks |
d264147017 | We investigate the capabilities of transformer large language models (LLMs) on relational reasoning tasks involving abstract symbols.Such tasks have long been studied in the neuroscience literature as fundamental building blocks for more complex abilities in programming, mathematics, and verbal reasoning.For (i) regres... | When can transformers reason with abstract symbols? |
d62823235 | Although stochastic gradient descent (SGD) method and its variants (e.g., stochastic momentum methods, ADAGRAD) are the choice of algorithms for solving nonconvex problems (especially deep learning), there still remain big gaps between the theory and the practice with many questions unresolved. For example, there is st... | Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions |
d235293845 | To successfully negotiate a deal, it is not enough to communicate fluently: pragmatic planning of persuasive negotiation strategies is essential. While modern dialogue agents excel at generating fluent sentences, they still lack pragmatic grounding and cannot reason strategically. We present DIALOGRAPH, a negotiation s... | DIALOGRAPH: INCORPORATING INTERPRETABLE STRATEGY-GRAPH NETWORKS INTO NEGOTIATION DIALOGUES |
d248964978 | In-context learning of GPT-like models has been recognized as fragile across different hand-crafted templates, and demonstration permutations. In this work, we propose prototypical calibration to adaptively learn a more robust decision boundary for zero-and few-shot classification, instead of greedy decoding. Concretel... | Prototypical Calibration for Few-shot Learning of Language Models |
d227338206 | Contrastive divergence (CD) learning is a classical method for fitting unnormalized statistical models to data samples. Despite its wide-spread use, the convergence properties of this algorithm are still not well understood. The main source of difficulty is an unjustified approximation which has been used to derive the... | CONTRASTIVE DIVERGENCE LEARNING IS A TIME REVERSAL ADVERSARIAL GAME |
d202750112 | We propose Dynamically Pruned Message Passing Networks (DPMPN) for largescale knowledge graph reasoning. In contrast to existing models, embeddingbased or path-based, we learn an input-dependent subgraph to explicitly model a sequential reasoning process. Each subgraph is dynamically constructed, expanding itself selec... | Dynamically Pruned Message Passing Networks for Large-Scale Knowledge Graph Reasoning |
d254044070 | Reliable application of machine learning-based decision systems in the wild is one of the major challenges currently investigated by the field. A large portion of established approaches aims to detect erroneous predictions by means of assigning confidence scores. This confidence may be obtained by either quantifying th... | A CALL TO REFLECT ON EVALUATION PRACTICES FOR FAILURE DETECTION IN IMAGE CLASSIFICATION |
d235614202 | We tackle a challenging blind image denoising problem, in which only single distinct noisy images are available for training a denoiser, and no information about noise is known, except for it being zero-mean, additive, and independent of the clean image. In such a setting, which often occurs in practice, it is not poss... | GAN2GAN: GENERATIVE NOISE LEARNING FOR BLIND DENOISING WITH SINGLE NOISY IMAGES |
d263334080 | Mixture models are traditionally represented and learned by adding several distributions as components.Allowing mixtures to subtract probability mass or density can drastically reduce the number of components needed to model complex distributions.However, learning such subtractive mixtures while ensuring they still enc... | SUBTRACTIVE MIXTURE MODELS VIA SQUARING: REPRESENTATION AND LEARNING |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.