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d261696510 | Inductive reasoning is a core problem-solving capacity: humans can identify underlying principles from a few examples, which can then be robustly generalized to novel scenarios. Recent work has evaluated large language models (LLMs) on inductive reasoning tasks by directly prompting them yielding "in context learning."... | HYPOTHESIS SEARCH: INDUCTIVE REASONING WITH LANGUAGE MODELS |
d252367996 | We show that combining human prior knowledge with end-to-end learning can improve the robustness of deep neural networks by introducing a part-based model for object classification. We believe that the richer form of annotation helps guide neural networks to learn more robust features without requiring more samples or ... | PART-BASED MODELS IMPROVE ADVERSARIAL ROBUSTNESS |
d9665638 | We propose a metric learning framework for the construction of invariant geometric functions of planar curves for the Euclidean and Similarity group of transformations. We leverage on the representational power of convolutional neural networks to compute these geometric quantities. In comparison with axiomatic construc... | LEARNING INVARIANT REPRESENTATIONS OF PLANAR CURVES |
d222133031 | Advances in generative modeling and adversarial learning have given rise to renewed interest in smooth games. However, the absence of symmetry in the matrix of second derivatives poses challenges that are not present in the classical minimization framework. While a rich theory of average-case analysis has been develope... | Average-case Acceleration for Bilinear Games and Normal Matrices |
d247451000 | Training very deep neural networks is still an extremely challenging task. The common solution is to use shortcut connections and normalization layers, which are both crucial ingredients in the popular ResNet architecture. However, there is strong evidence to suggest that ResNets behave more like ensembles of shallower... | DEEP LEARNING WITHOUT SHORTCUTS: SHAPING THE KERNEL WITH TAILORED RECTIFIERS |
d263909429 | We present a novel approach named OmniControl for incorporating flexible spatial control signals into a text-conditioned human motion generation model based on the diffusion process.Unlike previous methods that can only control the pelvis trajectory, OmniControl can incorporate flexible spatial control signals over dif... | OMNICONTROL: CONTROL ANY JOINT AT ANY TIME FOR HUMAN MOTION GENERATION |
d210920362 | Molecular graph generation is a fundamental problem for drug discovery and has been attracting growing attention. The problem is challenging since it requires not only generating chemically valid molecular structures but also optimizing their chemical properties in the meantime. Inspired by the recent progress in deep ... | GRAPHAF: A FLOW-BASED AUTOREGRESSIVE MODEL FOR MOLECULAR GRAPH GENERATION |
d264555202 | The interactive use of large language models (LLMs) in AI assistants (at work, home, etc.) introduces a new set of inference-time privacy risks: LLMs are fed different types of information from multiple sources in their inputs and are expected to reason about what to share in their outputs, for what purpose and with wh... | CAN LLMS KEEP A SECRET? TESTING PRIVACY IMPLICATIONS OF LANGUAGE MODELS VIA CONTEXTUAL INTEGRITY THEORY |
d245906072 | We study the dynamics of a neural network in function space when optimizing the mean squared error via gradient flow. We show that in the underparameterized regime the network learns eigenfunctions of an integral operator TK∞ determined by the Neural Tangent Kernel (NTK) at rates corresponding to their eigenvalues. For... | Implicit Bias of MSE Gradient Optimization in Underparameterized Neural Networks |
d252595883 | Natural and expressive human motion generation is the holy grail of computer animation. It is a challenging task, due to the diversity of possible motion, human perceptual sensitivity to it, and the difficulty of accurately describing it. Therefore, current generative solutions are either low-quality or limited in expr... | HUMAN MOTION DIFFUSION MODEL |
d263835059 | The Euler Characteristic Transform (ECT) has proven to be a powerful representation, combining geometrical and topological characteristics of shapes and graphs.However, the ECT was hitherto unable to learn task-specific representations.We overcome this issue and develop a novel computational layer that enables learning... | Differentiable Euler Characteristic Transforms for Shape Classification |
d256808748 | The ability to decompose complex natural scenes into meaningful object-centric abstractions lies at the core of human perception and reasoning. In the recent culmination of unsupervised object-centric learning, the Slot-Attention module has played an important role with its simple yet effective design and fostered many... | IMPROVING OBJECT-CENTRIC LEARNING WITH QUERY OPTIMIZATION |
d53452703 | Continuous Bag of Words (CBOW) is a powerful text embedding method. Due to its strong capabilities to encode word content, CBOW embeddings perform well on a wide range of downstream tasks while being efficient to compute. However, CBOW is not capable of capturing the word order. The reason is that the computation of CB... | CBOW IS NOT ALL YOU NEED: COMBINING CBOW WITH THE COMPOSITIONAL MATRIX SPACE MODEL |
d208857696 | Bayesian learning of model parameters in neural networks is important in scenarios where estimates with well-calibrated uncertainty are important. In this paper, we propose Bayesian quantized networks (BQNs), quantized neural networks (QNNs) for which we learn a posterior distribution over their discrete parameters. We... | SAMPLING-FREE LEARNING OF BAYESIAN QUANTIZED NEURAL NETWORKS |
d232320210 | We propose a novel information bottleneck (IB) method named Drop-Bottleneck, which discretely drops features that are irrelevant to the target variable.Drop-Bottleneck not only enjoys a simple and tractable compression objective but also additionally provides a deterministic compressed representation of the input varia... | Drop-Bottleneck: LEARNING DISCRETE COMPRESSED REPRESENTATION FOR NOISE-ROBUST EXPLORATION |
d219792087 | This paper proposes a novel differentiable architecture search method by formulating it into a distribution learning problem. We treat the continuously relaxed architecture mixing weight as random variables, modeled by Dirichlet distribution. With recently developed pathwise derivatives, the Dirichlet parameters can be... | DrNAS: Dirichlet Neural Architecture Search |
d247748808 | Adversarial Training (AT) is known as an effective approach to enhance the robustness of deep neural networks. Recently researchers notice that robust models with AT have good generative ability and can synthesize realistic images, while the reason behind it is yet under-explored. In this paper, we demystify this pheno... | A UNIFIED CONTRASTIVE ENERGY-BASED MODEL FOR UNDERSTANDING THE GENERATIVE ABILITY OF ADVERSARIAL TRAINING |
d222141668 | Human-annotated labels are often prone to noise, and the presence of such noise will degrade the performance of the resulting deep neural network (DNN) models. Much of the literature (with several recent exceptions) of learning with noisy labels focuses on the case when the label noise is independent from features. Pra... | Learning with Instance-Dependent Label Noise: A Sample Sieve Approach |
d204734206 | Intriguing empirical evidence exists that deep learning can work well with exotic schedules for varying the learning rate. This paper suggests that the phenomenon may be due to Batch Normalization or BN(Ioffe & Szegedy, 2015), which is ubiquitous and provides benefits in optimization and generalization across all stand... | AN EXPONENTIAL LEARNING RATE SCHEDULE FOR DEEP LEARNING |
d256868547 | Causal discovery from time-series data has been a central task in machine learning. Recently, Granger causality inference is gaining momentum due to its good explainability and high compatibility with emerging deep neural networks. However, most existing methods assume structured input data and degenerate greatly when ... | CUTS: NEURAL CAUSAL DISCOVERY FROM IRREGULAR TIME-SERIES DATA |
d236881207 | State-of-the-art deep face recognition methods are mostly trained with a softmaxbased multi-class classification framework. Despite being popular and effective, these methods still have a few shortcomings that limit empirical performance. In this paper, we start by identifying the discrepancy between training and evalu... | SPHEREFACE2: BINARY CLASSIFICATION IS ALL YOU NEED FOR DEEP FACE RECOGNITION |
d11243593 | We introduce a new model, the Recurrent Entity Network (EntNet). It is equipped with a dynamic long-term memory which allows it to maintain and update a representation of the state of the world as it receives new data. For language understanding tasks, it can reason on-the-fly as it reads text, not just when it is requ... | TRACKING THE WORLD STATE WITH RECURRENT ENTITY NETWORKS |
d30745030 | Neural networks exhibit good generalization behavior in the over-parameterized regime, where the number of network parameters exceeds the number of observations. Nonetheless, current generalization bounds for neural networks fail to explain this phenomenon. In an attempt to bridge this gap, we study the problem of lear... | SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data |
d3525802 | In this work we propose a simple and efficient framework for learning sentence representations from unlabelled data. Drawing inspiration from the distributional hypothesis and recent work on learning sentence representations, we reformulate the problem of predicting the context in which a sentence appears as a classifi... | AN EFFICIENT FRAMEWORK FOR LEARNING SENTENCE REPRESENTATIONS |
d246634167 | Principal component analysis is a simple yet useful dimensionality reduction technique in modern machine learning pipelines. In consequential domains such as college admission, healthcare and credit approval, it is imperative to take into account emerging criteria such as the fairness and the robustness of the learned ... | DISTRIBUTIONALLY ROBUST FAIR PRINCIPAL COMPONENTS VIA GEODESIC DESCENTS |
d53081529 | While the celebrated Word2Vec technique yields semantically rich representations for individual words, there has been relatively less success in extending to generate unsupervised sentences or documents embeddings. Recent work has demonstrated that a distance measure between documents called Word Mover's Distance (WMD)... | Word Mover's Embedding: From Word2Vec to Document Embedding |
d203642015 | We introduce ES-MAML, a new framework for solving the model agnostic meta learning (MAML) problem based on Evolution Strategies (ES). Existing algorithms for MAML are based on policy gradients, and incur significant difficulties when attempting to estimate second derivatives using backpropagation on stochastic policies... | ES-MAML: Simple Hessian-Free Meta Learning |
d256627797 | A soft tree is an actively studied variant of a decision tree that updates splitting rules using the gradient method. Although soft trees can take various architectures, their impact is not theoretically well known. In this paper, we formulate and analyze the Neural Tangent Kernel (NTK) induced by soft tree ensembles f... | Analyzing Tree Architectures in Ensembles via Neural Tangent Kernel |
d997870 | Standard model-free deep reinforcement learning (RL) algorithms sample a new initial state for each trial, allowing them to optimize policies that can perform well even in highly stochastic environments. However, problems that exhibit considerable initial state variation typically produce high-variance gradient estimat... | DIVIDE-AND-CONQUER REINFORCEMENT LEARNING |
d245124024 | Variational Autoencoders (VAEs) are one of the most commonly used generative models, particularly for image data. A prominent difficulty in training VAEs is data that is supported on a lower dimensional manifold. Recent work by Dai and Wipf (2020) proposes a two-stage training algorithm for VAEs, based on a conjecture ... | Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias |
d221139843 | Classifiers in machine learning are often brittle when deployed.Particularly concerning are models with inconsistent performance on specific subgroups of a class, e.g., exhibiting disparities in skin cancer classification in the presence or absence of a spurious bandage.To mitigate these performance differences, we int... | Model Patching: Closing the Subgroup Performance Gap with Data Augmentation |
d258461359 | Despite recent successes with neural models for sign language translation (SLT), translation quality still lags behind spoken languages because of the data scarcity and modality gap between sign video and text. To address both problems, we investigate strategies for cross-modality representation sharing for SLT. We pro... | SLTUNET: A SIMPLE UNIFIED MODEL FOR SIGN LANGUAGE TRANSLATION |
d246867279 | Deep neural network (DNN) classifiers are often overconfident, producing miscalibrated class probabilities. In high-risk applications like healthcare, practitioners require fully calibrated probability predictions for decision-making. That is, conditioned on the prediction vector, every class' probability should be clo... | TAKING A STEP BACK WITH KCAL: MULTI-CLASS KERNEL-BASED CALIBRATION FOR DEEP NEURAL NETWORKS |
d239616399 | To attain higher efficiency, the industry has gradually reformed towards applicationspecific hardware accelerators. While such a paradigm shift is already starting to show promising results, designers need to spend considerable manual effort and perform large number of time-consuming simulations to find accelerators th... | DATA-DRIVEN OFFLINE OPTIMIZATION FOR ARCHITECTING HARDWARE ACCELERATORS |
d220665925 | Few-shot classification (FSC), the task of adapting a classifier to unseen classes given a small labeled dataset, is an important step on the path toward human-like machine learning. Bayesian methods are well-suited to tackling the fundamental issue of overfitting in the few-shot scenario because they allow practitione... | Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes |
d263611938 | Neural networks trained by gradient descent (GD) have exhibited a number of surprising generalization behaviors. First, they can achieve a perfect fit to noisy training data and still generalize near-optimally, showing that overfitting can sometimes be benign. Second, they can undergo a period of classical, harmful ove... | Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data |
d213969759 | Mutual Information (MI) plays an important role in representation learning. However, MI is unfortunately intractable in continuous and high-dimensional settings. Recent advances establish tractable and scalable MI estimators to discover useful representation. However, most of the existing methods are not capable of pro... | MUTUAL INFORMATION GRADIENT ESTIMATION FOR REPRESENTATION LEARNING |
d34984289 | We propose SEARNN, a novel training algorithm for recurrent neural networks (RNNs) inspired by the "learning to search" (L2S) approach to structured prediction. RNNs have been widely successful in structured prediction applications such as machine translation or parsing, and are commonly trained using maximum likelihoo... | SEARNN: Training RNNs with global-local losses |
d239009452 | We present a novel methodology for repairing neural networks that use ReLU activation functions. Unlike existing methods that rely on modifying the weights of a neural network which can induce a global change in the function space, our approach applies only a localized change in the function space while still guarantee... | SOUND AND COMPLETE NEURAL NETWORK REPAIR WITH MINIMALITY AND LOCALITY GUARANTEES |
d235313504 | This paper considers minimax optimization minx maxy f (x, y) in the challenging setting where f can be both nonconvex in x and nonconcave in y. Though such optimization problems arise in many machine learning paradigms including training generative adversarial networks (GANs) and adversarially robust models, many funda... | Minimax Optimization with Smooth Algorithmic Adversaries |
d258714845 | A central task in computational drug discovery is to construct models from known active molecules to find further promising molecules for subsequent screening. However, typically only very few active molecules are known. Therefore, few-shot learning methods have the potential to improve the effectiveness of this critic... | CONTEXT-ENRICHED MOLECULE REPRESENTATIONS IMPROVE FEW-SHOT DRUG DISCOVERY |
d222272443 | Forecasting complex dynamical phenomena in settings where only partial knowledge of their dynamics is available is a prevalent problem across various scientific fields. While purely data-driven approaches are arguably insufficient in this context, standard physical modeling based approaches tend to be over-simplistic, ... | Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting |
d264172240 | The growing dependence on machine learning in real-world applications emphasizes the importance of understanding and ensuring its safety.Backdoor attacks pose a significant security risk due to their stealthy nature and potentially serious consequences.Such attacks involve embedding triggers within a learning model wit... | DEMYSTIFYING POISONING BACKDOOR ATTACKS FROM A STATISTICAL PERSPECTIVE |
d249625545 | Unsupervised domain adaptation (UDA) aims at learning a machine learning model using a labeled source domain that performs well on a similar yet different, unlabeled target domain. UDA is important in many applications such as medicine, where it is used to adapt risk scores across different patient cohorts. In this pap... | CONTRASTIVE LEARNING FOR UNSUPERVISED DOMAIN ADAPTATION OF TIME SERIES |
d3521071 | Intelligent creatures can explore their environments and learn useful skills without supervision. In this paper, we propose "Diversity is All You Need"(DIAYN), a method for learning useful skills without a reward function. Our proposed method learns skills by maximizing an information theoretic objective using a maximu... | DIVERSITY IS ALL YOU NEED: LEARNING SKILLS WITHOUT A REWARD FUNCTION |
d263829506 | Fusion is a technique for merging multiple independently-trained neural networks in order to combine their capabilities.Past attempts have been restricted to the case of fully-connected, convolutional, and residual networks.In this paper, we present a systematic approach for fusing two or more transformer-based network... | TRANSFORMER FUSION WITH OPTIMAL TRANSPORT |
d52986403 | This paper proposes a neural end-to-end text-to-speech (TTS) model which can control latent attributes in the generated speech that are rarely annotated in the training data, such as speaking style, accent, background noise, and recording conditions. The model is formulated as a conditional generative model with two le... | HIERARCHICAL GENERATIVE MODELING FOR CONTROLLABLE SPEECH SYNTHESIS |
d253237975 | Adaptive gradient methods have shown their ability to adjust the stepsizes on the fly in a parameteragnostic manner, and empirically achieve faster convergence for solving minimization problems. When it comes to nonconvex minimax optimization, however, current convergence analyses of gradient descent ascent (GDA) combi... | TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization |
d252846418 | Despite the clear performance benefits of data augmentations, little is known about why they are so effective. In this paper, we disentangle several key mechanisms through which data augmentations operate. Establishing an exchange rate between augmented and additional real data, we find that in out-of-distribution test... | HOW MUCH DATA ARE AUGMENTATIONS WORTH? AN INVESTIGATION INTO SCALING LAWS, INVARIANCE, AND IMPLICIT REGULARIZATION |
d246430268 | The release of tabular benchmarks, such as NAS-Bench-101 and NAS-Bench-201, has significantly lowered the computational overhead for conducting scientific research in neural architecture search (NAS). Although they have been widely adopted and used to tune real-world NAS algorithms, these benchmarks are limited to smal... | NAS-BENCH-SUITE: NAS EVALUATION IS (NOW) SURPRISINGLY EASY |
d222209080 | Self-training algorithms, which train a model to fit pseudolabels predicted by another previously-learned model, have been very successful for learning with unlabeled data using neural networks. However, the current theoretical understanding of self-training only applies to linear models. This work provides a unified t... | Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data |
d263829737 | Universal domain adaptation aims to align the classes and reduce the feature gap between the same category of the source and target domains.The target private category is set as the unknown class during the adaptation process, as it is not included in the source domain.However, most existing methods overlook the intra-... | Memory-Assisted Sub-Prototype Mining for Universal Domain Adaptation |
d211132867 | Training a classifier over a large number of classes, known as 'extreme classification', has become a topic of major interest with applications in technology, science, and e-commerce. Traditional softmax regression induces a gradient cost proportional to the number of classes C, which often is prohibitively expensive. ... | EXTREME CLASSIFICATION VIA ADVERSARIAL SOFTMAX APPROXIMATION |
d211010860 | This paper presents a novel physics-inspired deep learning approach for point cloud processing motivated by the natural flow phenomena in fluid mechanics. Our learning architecture jointly defines data in an Eulerian world space, using a static background grid, and a Lagrangian material space, using moving particles. B... | ADVECTIVENET: AN EULERIAN-LAGRANGIAN FLUIDIC RESERVOIR FOR POINT CLOUD PROCESSING |
d238583191 | Self-supervised learning (SSL) is a scalable way to learn general visual representations since it learns without labels. However, large-scale unlabeled datasets in the wild often have long-tailed label distributions, where we know little about the behavior of SSL. In this work, we systematically investigate self-superv... | Self-supervised Learning is More Robust to Dataset Imbalance |
d238419007 | Counterfactual examples are one of the most commonly-cited methods for explaining the predictions of machine learning models in key areas such as finance and medical diagnosis. Counterfactuals are often discussed under the assumption that the model on which they will be used is static, but in deployment models may be p... | CONSISTENT COUNTERFACTUALS FOR DEEP MODELS |
d222177494 | Action and observation delays commonly occur in many Reinforcement Learning applications, such as remote control scenarios. We study the anatomy of randomly delayed environments, and show that partially resampling trajectory fragments in hindsight allows for off-policy multi-step value estimation. We apply this princip... | REINFORCEMENT LEARNING WITH RANDOM DELAYS |
d222125116 | Circuits of biological neurons, such as in the functional parts of the brain can be modeled as networks of coupled oscillators. Inspired by the ability of these systems to express a rich set of outputs while keeping (gradients of) state variables bounded, we propose a novel architecture for recurrent neural networks. O... | Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies |
d4679427 | We propose a new Integral Probability Metric (IPM) between distributions: the Sobolev IPM. The Sobolev IPM compares the mean discrepancy of two distributions for functions (critic) restricted to a Sobolev ball defined with respect to a dominant measure µ. We show that the Sobolev IPM compares two distributions in high ... | Sobolev GAN |
d219558760 | Graph neural networks (GNNs) were shown to effectively learn from highly structured data containing elements (nodes) with relationships (edges) between them. GNN variants differ in how each node in the graph absorbs the information flowing from its neighbor nodes. In this paper, we highlight an inherent problem in GNNs... | On the Bottleneck of Graph Neural Networks and its Practical Implications |
d88514953 | Background: Statistical mechanics results(Dauphin et al. (2014);Choromanska et al. (2015)) suggest that local minima with high error are exponentially rare in high dimensions. However, to prove low error guarantees for Multilayer Neural Networks (MNNs), previous works so far required either a heavily modified MNN model... | EXPONENTIALLY VANISHING SUB-OPTIMAL LOCAL MINIMA IN MULTILAYER NEURAL NETWORKS |
d52922902 | We propose a new learning-based approach to solve ill-posed inverse problems in imaging. We address the case where ground truth training samples are rare and the problem is severely ill-posed-both because of the underlying physics and because we can only get few measurements. This setting is common in geophysical imagi... | RANDOM MESH PROJECTORS FOR INVERSE PROBLEMS |
d214220671 | reasoning, particularly in the visual domain, is a complex human ability, but it remains a challenging problem for artificial neural learning systems. In this work we propose MXGNet, a multilayer graph neural network for multi-panel diagrammatic reasoning tasks. MXGNet combines three powerful concepts, namely, object-l... | ABSTRACT DIAGRAMMATIC REASONING WITH MULTIPLEX GRAPH NETWORKS |
d263671510 | The recently released GPT-4 Code Interpreter has demonstrated remarkable proficiency in solving challenging math problems, primarily attributed to its ability to seamlessly reason with natural language, generate code, execute code, and continue reasoning based on the execution output.In this paper, we present a method ... | MATHCODER: SEAMLESS CODE INTEGRATION IN LLMS FOR ENHANCED MATHEMATICAL REASONING |
d33513311 | Ongoing innovations in recurrent neural network architectures have provided a steady influx of apparently state-of-theart results on language modelling benchmarks. However, these have been evaluated using differing code bases and limited computational resources, which represent uncontrolled sources of experimental vari... | On the State of the Art of Evaluation in Neural Language Models |
d52978527 | Network pruning is widely used for reducing the heavy computational cost of deep models. A typical pruning algorithm is a three-stage pipeline, i.e., training (a large model), pruning and fine-tuning. During pruning, according to a certain criterion, redundant weights are pruned and important weights are kept to best p... | RETHINKING THE VALUE OF NETWORK PRUNING |
d204734475 | Infinite horizon off-policy policy evaluation is a highly challenging task due to the excessively large variance of typical importance sampling (IS) estimators. Recently, Liu et al. (2018a) proposed an approach that significantly reduces the variance of infinite-horizon off-policy evaluation by estimating the station... | Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation |
d254221022 | Multi-hop Question Answering over Knowledge Graph (KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question on a large-scale Knowledge Graph (KG). To cope with the vast search space, existing work usually adopts a two-stage approach: it first re... | UNIKGQA: UNIFIED RETRIEVAL AND REASONING FOR SOLVING MULTI-HOP QUESTION ANSWERING OVER KNOWLEDGE GRAPH |
d250627720 | We propose a novel framework for multitask reinforcement learning based on the minimum description length (MDL) principle. In this approach, which we term MDL-control (MDL-C), the agent learns the common structure among the tasks with which it is faced and then distills it into a simpler representation which facilitate... | Minimum Description Length Control |
d256615813 | High-quality instance segmentation has shown emerging importance in computer vision. Without any refinement, DCT-Mask directly generates high-resolution masks by compressed vectors. To further refine masks obtained by compressed vectors, we propose for the first time a compressed vector based multi-stage refinement fra... | PATCHDCT: PATCH REFINEMENT FOR HIGH QUALITY INSTANCE SEGMENTATION |
d14254027 | In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time. We show that our model, which profits from combining memory-less modules, namely autoregressive multilayer perceptrons, and stateful recurrent neural networks in a hierarchical structure is able to ... | SAMPLERNN: AN UNCONDITIONAL END-TO-END NEURAL AUDIO GENERATION MODEL |
d263609164 | We theoretically explore the relationship between sample-efficiency and adaptivity in reinforcement learning.An algorithm is sample-efficient if it uses a number of queries n to the environment that is polynomial in the dimension d of the problem.Adaptivity refers to the frequency at which queries are sent and feedback... | Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity |
d44096233 | In this paper, we study the problem of geometric reasoning in the context of question-answering. We introduce Dynamic Spatial Memory Network (DSMN), a new deep network architecture designed for answering questions that admit latent visual representations. DSMN learns to generate and reason over such representations. Fu... | Think Visually: Question Answering through Virtual Imagery |
d59600025 | Word embedding is a powerful tool in natural language processing. In this paper we consider the problem of word embedding composition -given vector representations of two words, compute a vector for the entire phrase. We give a generative model that can capture specific syntactic relations between words. Under our mode... | Understanding Composition of Word Embeddings via Tensor Decomposition |
d53729760 | Generative Adversarial Networks (GANs) have recently achieved impressive results for many real-world applications, and many GAN variants have emerged with improvements in sample quality and training stability. However, they have not been well visualized or understood. How does a GAN represent our visual world internall... | GAN DISSECTION: VISUALIZING AND UNDERSTANDING GENERATIVE ADVERSARIAL NETWORKS |
d220769181 | Recently, multi-agent policy gradient (MAPG) methods witness vigorous progress. However, there is a discrepancy between the performance of MAPG methods and state-of-the-art multi-agent value-based approaches. In this paper, we investigate the causes that hinder the performance of MAPG algorithms and present a multiagen... | DOP: Off-Policy Multi-Agent Decomposed Policy Gradients |
d3307812 | Robust real-world learning should benefit from both demonstrations and interactions with the environment. Current approaches to learning from demonstration and reward perform supervised learning on expert demonstration data and use reinforcement learning to further improve performance based on the reward received from ... | Reinforcement Learning from Imperfect Demonstrations |
d222341655 | Instrumental variable (IV) regression is a standard strategy for learning causal relationships between confounded treatment and outcome variables from observational data by using an instrumental variable, which affects the outcome only through the treatment. In classical IV regression, learning proceeds in two stages: ... | Learning Deep Features in Instrumental Variable Regression |
d252110923 | We study a modular approach to tackle long-horizon mobile manipulation tasks for object rearrangement, which decomposes a full task into a sequence of subtasks. To tackle the entire task, prior work chains multiple stationary manipulation skills with a point-goal navigation skill, which are learned individually on subt... | Multi-skill Mobile Manipulation for Object Rearrangement |
d211069439 | We present a deep generative model for unsupervised text style transfer that unifies previously proposed non-generative techniques. Our probabilistic approach models non-parallel data from two domains as a partially observed parallel corpus. By hypothesizing a parallel latent sequence that generates each observed seque... | A PROBABILISTIC FORMULATION OF UNSUPERVISED TEXT STYLE TRANSFER |
d264127928 | Phylogenetics is a branch of computational biology that studies the evolutionary relationships among biological entities.Its long history and numerous applications notwithstanding, inference of phylogenetic trees from sequence data remains challenging: the high complexity of tree space poses a significant obstacle for ... | PHYLOGFN: PHYLOGENETIC INFERENCE WITH GENERATIVE FLOW NETWORKS |
d232105052 | Transformers are state-of-the-art models for a variety of sequence modeling tasks.At their core is an attention function which models pairwise interactions between the inputs at every timestep.While attention is powerful, it does not scale efficiently to long sequences due to its quadratic time and space complexity in ... | RANDOM FEATURE ATTENTION |
d257102434 | When deployed for risk-sensitive tasks, deep neural networks must be able to detect instances with labels from outside the distribution for which they were trained. In this paper we present a novel framework to benchmark the ability of image classifiers to detect class-out-of-distribution instances (i.e., instances who... | A FRAMEWORK FOR BENCHMARKING CLASS-OUT-OF-DISTRIBUTION DETECTION AND ITS APPLICATION TO IMAGENET |
d225076054 | Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning. While disentangled representations were found to be useful for diverse tasks such as abstract reasoning and fair classification, their scalability and r... | On the Transfer of Disentangled Representations in Realistic Settings |
d11445252 | We introduce a new large-scale music dataset, MusicNet, to serve as a source of supervision and evaluation of machine learning methods for music research. Mu-sicNet consists of hundreds of freely-licensed classical music recordings by 10 composers, written for 11 instruments, together with instrument/note annotations r... | LEARNING FEATURES OF MUSIC FROM SCRATCH |
d225067229 | Dialogue state trackers have made significant progress on benchmark datasets, but their generalization capability to novel and realistic scenarios beyond the heldout conversations is less understood. We propose controllable counterfactuals (COCO) to bridge this gap and evaluate dialogue state tracking (DST) models on n... | COCO: CONTROLLABLE COUNTERFACTUALS FOR EVALUATING DIALOGUE STATE TRACKERS |
d215814169 | We tackle the problem of producing compact models, maximizing their accuracy for a given model size. A standard solution is to train networks with Quantization Aware Training [1], where the weights are quantized during training and the gradients approximated with the Straight-Through Estimator[2]. In this paper, we ext... | Training with Quantization Noise for Extreme Model Compression |
d264426451 | Consistency models are a nascent family of generative models that can sample high quality data in one step without the need for adversarial training.Current consistency models achieve optimal sample quality by distilling from pre-trained diffusion models and employing learned metrics such as LPIPS.However, distillation... | IMPROVED TECHNIQUES FOR TRAINING CONSISTENCY MODELS |
d235606439 | Frequently, population studies feature pyramidally-organized data represented using Hierarchical Bayesian Models (HBM) enriched with plates. These models can become prohibitively large in settings such as neuroimaging, where a sample is composed of a functional MRI signal measured on 300 brain locations, across 4 measu... | ADAVI: AUTOMATIC DUAL AMORTIZED VARIATIONAL INFERENCE APPLIED TO PYRAMIDAL BAYESIAN MODELS |
d47015748 | To act and plan in complex environments, we posit that agents should have a mental simulator of the world with three characteristics: (a) it should build an abstract state representing the condition of the world; (b) it should form a belief which represents uncertainty on the world; (c) it should go beyond simple step-... | TEMPORAL DIFFERENCE VARIATIONAL AUTO-ENCODER |
d263672042 | The theoretical landscape of federated learning (FL) undergoes rapid evolution, but its practical application encounters a series of intricate challenges, and hyperparameter optimization is one of these critical challenges.Amongst the diverse adjustments in hyperparameters, the adaptation of the learning rate emerges a... | FEDHYPER: A UNIVERSAL AND ROBUST LEARNING RATE SCHEDULER FOR FEDERATED LEARNING WITH HYPERGRADIENT DESCENT |
d263829348 | The past decade has witnessed significant advances in time series modeling with deep learning. While achieving state-of-the-art results, the best-performing architectures vary highly across applications and domains. Meanwhile, for natural language processing, the Generative Pre-trained Transformer (GPT) has demonstrate... | TEMPO: PROMPT-BASED GENERATIVE PRE-TRAINED TRANSFORMER FOR TIME SERIES FORECASTING |
d250920542 | The task of generating code solutions for a given programming problem can benefit from the use of pre-trained language models such as Codex, which can produce multiple diverse samples. However, a major challenge for this task is to select the most appropriate solution from the multiple samples generated by the pretrain... | CODET: CODE GENERATION WITH GENERATED TESTS |
d247476291 | Unsupervised semantic segmentation aims to discover and localize semantically meaningful categories within image corpora without any form of annotation. To solve this task, algorithms must produce features for every pixel that are both semantically meaningful and compact enough to form distinct clusters. Unlike previou... | UNSUPERVISED SEMANTIC SEGMENTATION BY DISTILLING FEATURE CORRESPONDENCES |
d235313882 | Deployment of machine learning models in real high-risk settings (e.g. healthcare) often depends not only on the model's accuracy but also on its fairness, robustness, and interpretability. Generalized Additive Models (GAMs) are a class of interpretable models with a long history of use in these high-risk domains, but ... | NODE-GAM: NEURAL GENERALIZED ADDITIVE MODEL FOR INTERPRETABLE DEEP LEARNING |
d252693361 | We propose a new class of deep reinforcement learning (RL) algorithms that model latent representations in hyperbolic space. Sequential decision-making requires reasoning about the possible future consequences of current behavior. Consequently, capturing the relationship between key evolving features for a given task i... | HYPERBOLIC DEEP REINFORCEMENT LEARNING |
d252683793 | Noise Contrastive Estimation (NCE) is a popular approach for learning probability density functions parameterized up to a constant of proportionality. The main idea is to use self-supervised learning (SSL): that is, construct a classification problem for distinguishing training data from samples from an easy-to-sample ... | Pitfalls of Gaussians as a noise distribution in NCE |
d259360601 | This paper studies the sample-efficiency of learning in Partially Observable Markov Decision Processes (POMDPs), a challenging problem in reinforcement learning that is known to be exponentially hard in the worst-case. Motivated by real-world settings such as loading in game playing, we propose an enhanced feedback mod... | Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight |
d253255190 | When trained on language data, do transformers learn some arbitrary computation that utilizes the full capacity of the architecture or do they learn a simpler, treelike computation, hypothesized to underlie compositional meaning systems like human languages? There is an apparent tension between compositional accounts o... | CHARACTERIZING INTRINSIC COMPOSITIONALITY IN TRANSFORMERS WITH TREE PROJECTIONS |
d1706438 | We propose a language-agnostic way of automatically generating sets of semantically similar clusters of entities along with sets of "outlier" elements, which may then be used to perform an intrinsic evaluation of word embeddings in the outlier detection task. We used our methodology to create a gold-standard dataset, w... | AUTOMATED GENERATION OF MULTILINGUAL CLUSTERS FOR THE EVALUATION OF DISTRIBUTED REPRESENTATIONS |
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