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d244799256 | Recent advances at the intersection of dense large graph limits and mean field games have begun to enable the scalable analysis of a broad class of dynamical sequential games with large numbers of agents. So far, results have been largely limited to graphon mean field systems with continuous-time diffusive or jump dyna... | LEARNING GRAPHON MEAN FIELD GAMES AND APPROXIMATE NASH EQUILIBRIA |
d243847413 | Federated learning is an established method for training machine learning models without sharing training data. However, recent work has shown that it cannot guarantee data privacy as shared gradients can still leak sensitive information. To formalize the problem of gradient leakage, we propose a theoretical framework ... | BAYESIAN FRAMEWORK FOR GRADIENT LEAKAGE |
d67770197 | Building agents to interact with the web would allow for significant improvements in knowledge understanding and representation learning. However, web navigation tasks are difficult for current deep reinforcement learning (RL) models due to the large discrete action space and the varying number of actions between the s... | DOM-Q-NET: GROUNDED RL ON STRUCTURED LANGUAGE |
d56657849 | High-dimensional time series are common in many domains. Since human cognition is not optimized to work well in high-dimensional spaces, these areas could benefit from interpretable low-dimensional representations. However, most representation learning algorithms for time series data are difficult to interpret. This is... | SOM-VAE: INTERPRETABLE DISCRETE REPRESENTATION LEARNING ON TIME SERIES |
d49876500 | Autoencoders provide a powerful framework for learning compressed representations by encoding all of the information needed to reconstruct a data point in a latent code. In some cases, autoencoders can "interpolate": By decoding the convex combination of the latent codes for two datapoints, the autoencoder can produce ... | Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer |
d264825357 | In this work, we propose a concise neural operator architecture for operator learning.Drawing an analogy with a conventional fully connected neural network, we define the neural operator as follows: the output of the i-th neuron in a nonlinear operator layer is defined by O i (u) = σ j W i j u + B i j .Here, W i j deno... | MgNO: Efficient Parameterization of Linear Operators via Multigrid |
d235386376 | Training and using modern neural-network based latent-variable generative models (like Variational Autoencoders) often require simultaneously training a generative direction along with an inferential (encoding) direction, which approximates the posterior distribution over the latent variables. Thus, the question arises... | The Effects of Invertibility on the Representational Complexity of Encoders in Variational Autoencoders |
d52297370 | We consider a problem of learning a reward and policy from expert examples under unknown dynamics in high-dimensional scenarios. Our proposed method builds on the framework of generative adversarial networks and exploits reward shaping to learn near-optimal rewards and policies. Potential-based reward shaping functions... | ADVERSARIAL IMITATION VIA VARIATIONAL INVERSE REINFORCEMENT LEARNING |
d263909446 | Diffusion models suffer from slow sample generation at inference time.Therefore, developing a principled framework for fast deterministic/stochastic sampling for a broader class of diffusion models is a promising direction.We propose two complementary frameworks for accelerating sample generation in pretrained models: ... | EFFICIENT INTEGRATORS FOR DIFFUSION GENERATIVE MODELS |
d244920632 | 3D point cloud is an important 3D representation for capturing real world 3D objects. However, real-scanned 3D point clouds are often incomplete, and it is important to recover complete point clouds for downstream applications. Most existing point cloud completion methods use Chamfer Distance (CD) loss for training. Th... | A CONDITIONAL POINT DIFFUSION-REFINEMENT PARADIGM FOR 3D POINT CLOUD COMPLETION |
d264825556 | Diffusion or flow-based models are powerful generative paradigms that are notoriously hard to sample as samples are defined as solutions to high-dimensional Ordinary or Stochastic Differential Equations (ODEs/SDEs) which require a large Number of Function Evaluations (NFE) to approximate well.Existing methods to allevi... | BESPOKE SOLVERS FOR GENERATIVE FLOW MODELS |
d235358868 | Active learning is the process of training a model with limited labeled data by selecting a core subset of an unlabeled data pool to label. The large scale of data sets used in deep learning forces most sample selection strategies to employ efficient heuristics. This paper introduces an integer optimization problem for... | LOW-BUDGET ACTIVE LEARNING VIA WASSERSTEIN DISTANCE: AN INTEGER PROGRAMMING APPROACH |
d256827824 | Modern ML applications increasingly rely on complex deep learning models and large datasets.There has been an exponential growth in the amount of computation needed to train the largest models.Therefore, to scale computation and data, these models are inevitably trained in a distributed manner in clusters of nodes, and... | Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization |
d259375820 | Recent works have empirically analyzed in-context learning and shown that transformers trained on synthetic linear regression tasks can learn to implement ridge regression, which is the Bayes-optimal predictor, given sufficient capacity [Akyürek et al., 2023], while one-layer transformers with linear selfattention and ... | One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention |
d246485738 | Deep learning has been actively studied for time series forecasting, and the mainstream paradigm is based on the end-to-end training of neural network architectures, ranging from classical LSTM/RNNs to more recent TCNs and Transformers. Motivated by the recent success of representation learning in computer vision and n... | COST: CONTRASTIVE LEARNING OF DISENTANGLED SEASONAL-TREND REPRESENTATIONS FOR TIME SERIES FORECASTING |
d49411844 | This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architectu... | DARTS: Differentiable Architecture Search |
d263334556 | Implicit neural representations (INRs) have arisen as useful methods for representing signals on Euclidean domains.By parameterizing an image as a multilayer perceptron (MLP) on Euclidean space, INRs effectively represent signals in a way that couples spatial and spectral features of the signal that is not obvious in t... | Implicit Neural Representations and the Algebra of Complex Wavelets |
d263909387 | Speculative decoding (SD) accelerates large language model inference by employing a faster draft model for generating multiple tokens, which are then verified in parallel by the larger target model, resulting in the text generated according to the target model distribution.However, identifying a compact draft model tha... | DISTILLSPEC: IMPROVING SPECULATIVE DECODING VIA KNOWLEDGE DISTILLATION |
d53034786 | In general, natural language is governed by a tree structure: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). This is a strict hierarchy: when a larger constituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM allows different ... | ORDERED NEURONS: INTEGRATING TREE STRUCTURES INTO RECURRENT NEURAL NETWORKS |
d256827026 | Large-scale vision-language pre-trained models have shown promising transferability to various downstream tasks. As the size of these foundation models and the number of downstream tasks grow, the conventional full fine-tuning paradigm becomes impractical due to heavy computational and storage costs. This paper propose... | UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling |
d263334567 | Transformers have become the standard in state-of-the-art vision architectures, achieving impressive performance on both image-level and dense pixelwise tasks.However, training vision transformers for high-resolution pixelwise tasks has a prohibitive cost.Typical solutions boil down to hierarchical architectures, fast ... | WIN-WIN: TRAINING HIGH-RESOLUTION VISION TRANSFORMERS FROM TWO WINDOWS |
d252715598 | This paper presents MOAT, a family of neural networks that build on top of MObile convolution (i.e., inverted residual blocks) and ATtention. Unlike the current works that stack separate mobile convolution and transformer blocks, we effectively merge them into a MOAT block. Starting with a standard Transformer block, w... | MOAT: ALTERNATING MOBILE CONVOLUTION AND ATTENTION BRINGS STRONG VISION MODELS |
d53113014 | Computer simulation provides an automatic and safe way for training robotic control policies to achieve complex tasks such as locomotion. However, a policy trained in simulation usually does not transfer directly to the real hardware due to the differences between the two environments. Transfer learning using domain ra... | POLICY TRANSFER WITH STRATEGY OPTIMIZATION |
d237372712 | We present miniF2F, a dataset of formal Olympiad-level mathematics problems statements intended to provide a unified cross-system benchmark for neural theorem proving. The miniF2F benchmark currently targets Metamath, Lean, Isabelle (partially) and HOL Light (partially) and consists of 488 problem statements drawn from... | MINIF2F: A CROSS-SYSTEM BENCHMARK FOR FORMAL OLYMPIAD-LEVEL MATHEMATICS |
d246294898 | Self-supervised protein language models have proved their effectiveness in learning the proteins representations. With the increasing computational power, current protein language models pre-trained with millions of diverse sequences can advance the parameter scale from million-level to billion-level and achieve remark... | ONTOPROTEIN: PROTEIN PRETRAINING WITH GENE ONTOLOGY EMBEDDING |
d259187750 | This paper introduces the Fair Fairness Benchmark (FFB), a benchmarking framework for in-processing group fairness methods. Ensuring fairness in machine learning is critical for ethical and legal compliance. However, there exist challenges in comparing and developing of fairness methods due to inconsistencies in experi... | FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods |
d52920337 | This paper establishes risk convergence and asymptotic weight matrix alignment -a form of implicit regularization -of gradient flow and gradient descent when applied to deep linear networks on linearly separable data. In more detail, for gradient flow applied to strictly decreasing loss functions (with similar results ... | Gradient descent aligns the layers of deep linear networks |
d247693295 | Models of human behavior for prediction and collaboration tend to fall into two categories: ones that learn from large amounts of data via imitation learning, and ones that assume human behavior to be noisily-optimal for some reward function. The former are very useful, but only when it is possible to gather a lot of h... | THE BOLTZMANN POLICY DISTRIBUTION: ACCOUNTING FOR SYSTEMATIC SUBOPTIMALITY IN HUMAN MODELS |
d235212307 | The randomized singular value decomposition (SVD) is a popular and effective algorithm for computing a near-best rank k approximation of a matrix A using matrix-vector products with standard Gaussian vectors. Here, we generalize the randomized SVD to multivariate Gaussian vectors, allowing one to incorporate prior know... | A GENERALIZATION OF THE RANDOMIZED SINGULAR VALUE DECOMPOSITION |
d238419003 | Cross-domain imitation learning studies how to leverage expert demonstrations of one agent to train an imitation agent with a different embodiment or morphology. Comparing trajectories and stationary distributions between the expert and imitation agents is challenging because they live on different systems that may not... | CROSS-DOMAIN IMITATION LEARNING VIA OPTIMAL TRANSPORT |
d23387956 | The Fisher information metric is an important foundation of information geometry, wherein it allows us to approximate the local geometry of a probability distribution. Recurrent neural networks such as the Sequence-to-Sequence (Seq2Seq) networks that have lately been used to yield state-of-the-art performance on speech... | GEOSEQ2SEQ: INFORMATION GEOMETRIC SEQUENCE-TO-SEQUENCE NETWORKS |
d52920181 | Rewards are sparse in the real world and most today's reinforcement learning algorithms struggle with such sparsity. One solution to this problem is to allow the agent to create rewards for itself -thus making rewards dense and more suitable for learning. In particular, inspired by curious behaviour in animals, observi... | EPISODIC CURIOSITY THROUGH REACHABILITY |
d254877694 | Text classifiers have promising applications in high-stake tasks such as resume screening and content moderation. These classifiers must be fair and avoid discriminatory decisions by being invariant to perturbations of sensitive attributes such as gender or ethnicity. However, there is a gap between human intuition abo... | HUMAN-GUIDED FAIR CLASSIFICATION FOR NATURAL LANGUAGE PROCESSING |
d220496457 | Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalize well and are robust to changes in the input distribution. While methods that harness spatial and temporal structures find broad application, recent work has demonstrated the potenti... | Spatially Structured Recurrent Modules |
d264172710 | As large language models (LLMs) are adopted as a fundamental component of language technologies, it is crucial to accurately characterize their performance. Because choices in prompt design can strongly influence model behavior, this design process is critical in effectively using any modern pre-trained generative lang... | QUANTIFYING LANGUAGE MODELS' SENSITIVITY TO SPURIOUS FEATURES IN PROMPT DESIGN or: How I learned to start worrying about prompt formatting |
d253098972 | Prompt tuning approaches, which learn task-specific soft prompts for a downstream task conditioning on frozen pre-trained models, have attracted growing interest due to its parameter efficiency. With large language models and sufficient training data, prompt tuning performs comparably to full-model tuning. However, wit... | MODEL ENSEMBLE INSTEAD OF PROMPT FUSION: A SAMPLE-SPECIFIC KNOWLEDGE TRANSFER METHOD FOR FEW-SHOT PROMPT TUNING |
d253237991 | We consider the problem of clustering in the learning-augmented setting, where we are given a data set in d-dimensional Euclidean space, and a label for each data point given by an oracle indicating what subsets of points should be clustered together. This setting captures situations where we have access to some auxili... | Improved Learning-augmented Algorithms for k-means and k-medians Clustering |
d254535963 | Inferring reward functions from human behavior is at the center of value alignment -aligning AI objectives with what we, humans, actually want.But doing so relies on models of how humans behave given their objectives.After decades of research in cognitive science, neuroscience, and behavioral economics, obtaining accur... | ON THE SENSITIVITY OF REWARD INFERENCE TO MISSPECIFIED HUMAN MODELS |
d263605735 | Dueling bandits is a prominent framework for decision-making involving preferential feedback, a valuable feature that fits various applications involving human interaction, such as ranking, information retrieval, and recommendation systems.While substantial efforts have been made to minimize the cumulative regret in du... | Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits |
d35432793 | We study the properties of common loss surfaces through their Hessian matrix. In particular, in the context of deep learning, we empirically show that the spectrum of the Hessian is composed of two parts: (1) the bulk centered near zero, (2) and outliers away from the bulk. We present numerical evidence and mathematica... | Empirical Analysis of the Hessian of Over-Parametrized Neural Networks |
d258865444 | In this work we present an approach for generating alternative text (or alt-text) descriptions for images shared on social media, specifically Twitter.More than just a special case of image captioning, alt-text is both more literally descriptive and context-specific.Also critically, images posted to Twitter are often a... | ALT-TEXT WITH CONTEXT: IMPROVING ACCESSIBILITY FOR IMAGES ON TWITTER |
d257687492 | Quality Diversity (QD) has emerged as a powerful alternative optimization paradigm that aims at generating large and diverse collections of solutions, notably with its flagship algorithm MAP-ELITES (ME) which evolves solutions through mutations and crossovers. While very effective for some unstructured problems, early ... | EVOLVING POPULATIONS OF DIVERSE RL AGENTS WITH MAP-ELITES |
d3497822 | Recurrent neural networks (RNNs) are widely used to model sequential data but their non-linear dependencies between sequence elements prevent parallelizing training over sequence length. We show the training of RNNs with only linear sequential dependencies can be parallelized over the sequence length using the parallel... | Parallelizing Linear Recurrent Neural Nets Over Sequence Length |
d261100891 | Machine learning models are often used to decide who will receive a loan, a job interview, or a public benefit. Standard techniques to build these models use features about people but overlook their actionability. In turn, models can assign predictions that are fixed -meaning that consumers who are denied loans, interv... | Prediction without Preclusion: Recourse Verification with Reachable Sets |
d27494814 | Model pruning seeks to induce sparsity in a deep neural network's various connection matrices, thereby reducing the number of nonzero-valued parameters in the model. Recent reports(Han et al., 2015a;Narang et al., 2017)prune deep networks at the cost of only a marginal loss in accuracy and achieve a sizable reduction i... | To prune, or not to prune: exploring the efficacy of pruning for model compression |
d3340951 | The large memory requirements of deep neural networks limit their deployment and adoption on many devices. Model compression methods effectively reduce the memory requirements of these models, usually through applying transformations such as weight pruning or quantization. In this paper, we present a novel scheme for l... | WEIGHTLESS: LOSSY WEIGHT ENCODING FOR DEEP NEURAL NETWORK COMPRESSION |
d249625810 | We consider the standard K-armed bandit problem under a distributed trust model of differential privacy (DP), which enables to guarantee privacy without a trustworthy server. Under this trust model, previous work largely focus on achieving privacy using a shuffle protocol, where a batch of users data are randomly permu... | Distributed Differential Privacy in Multi-Armed Bandits |
d9725544 | Given the recent successes of deep learning applied to style transfer and texture synthesis, we propose a new theoretical framework to construct visual metamers: a family of perceptually identical, yet physically different images. We review work both in neuroscience related to metameric stimuli, as well as computer vis... | Towards Metamerism via Foveated Style Transfer |
d235417313 | Federated learning has evolved to improve a single global model under data heterogeneity (as a curse) or to develop multiple personalized models using data heterogeneity (as a blessing). However, little research has considered both directions simultaneously. In this paper, we first investigate the relationship between ... | FEDBABU: TOWARD ENHANCED REPRESENTATION FOR FEDERATED IMAGE CLASSIFICATION |
d220249871 | How to explicitly encode positional information into neural networks is important in learning the representation of natural languages, such as BERT. Based on the Transformer architecture, the positional information is simply encoded as embedding vectors, which are used in the input layer, or encoded as a bias term in t... | RETHINKING POSITIONAL ENCODING IN LANGUAGE PRE-TRAINING |
d60440615 | Multilingual training of neural machine translation (NMT) systems has led to impressive accuracy improvements on low-resource languages. However, there are still significant challenges in efficiently learning word representations in the face of paucity of data. In this paper, we propose Soft Decoupled Encoding (SDE), a... | MULTILINGUAL NEURAL MACHINE TRANSLATION WITH SOFT DECOUPLED ENCODING |
d252762329 | Recently, researchers observed that gradient descent for deep neural networks operates in an "edge-ofstability" (EoS) regime: the sharpness (maximum eigenvalue of the Hessian) is often larger than stability threshold 2/η (where η is the step size). Despite this, the loss oscillates and converges in the long run, and th... | Understanding Edge-of-Stability Training Dynamics with a Minimalist Example |
d253098739 | Meta-learning aims to extract useful inductive biases from a set of related datasets. In Bayesian meta-learning, this is typically achieved by constructing a prior distribution over neural network parameters. However, specifying families of computationally viable prior distributions over the high-dimensional neural net... | MARS: META-LEARNING AS SCORE MATCHING IN THE FUNCTION SPACE |
d251710555 | Standard inference and training with transformer based architectures scale quadratically with input sequence length. This is prohibitively large for a variety of applications especially in web-page translation, query-answering etc. Consequently, several approaches have been developed recently to speedup attention compu... | TREEFORMER: DENSE GRADIENT TREES FOR EFFICIENT ATTENTION COMPUTATION |
d204509033 | Variational approaches based on neural networks are showing promise for estimating mutual information (MI) between high dimensional variables. However, they can be difficult to use in practice due to poorly understood bias/variance tradeoffs. We theoretically show that, under some conditions, estimators such as MINE ex... | UNDERSTANDING THE LIMITATIONS OF VARIATIONAL MUTUAL INFORMATION ESTIMATORS |
d251197051 | Recent Language Models (LMs) achieve breakthrough performance in code generation when trained on human-authored problems, even solving some competitive-programming problems. Self-play has proven useful in games such as Go, and thus it is natural to ask whether LMs can generate their own instructive programming problems... | Language Models Can Teach Themselves to Program Better |
d263310678 | The massive interest in deep neural networks (DNNs) for both computer vision and natural language processing has been sparked by the growth in computational power.However, this led to an increase in the memory footprint, to a point where it can be challenging to simply load a model on commodity devices such as mobile p... | NETWORK MEMORY FOOTPRINT COMPRESSION THROUGH JOINTLY LEARNABLE CODEBOOKS AND MAPPINGS |
d248266388 | The reinforcement learning (RL) problem is rife with sources of non-stationarity, making it a notoriously difficult problem domain for the application of neural networks. We identify a mechanism by which non-stationary prediction targets can prevent learning progress in deep RL agents: capacity loss, whereby networks t... | UNDERSTANDING AND PREVENTING CAPACITY LOSS IN REINFORCEMENT LEARNING |
d221447287 | This paper introduces WaveGrad, a conditional model for waveform generation through estimating gradients of the data density. This model is built on the prior work on score matching and diffusion probabilistic models. It starts from Gaussian white noise and iteratively refines the signal via a gradient-based sampler co... | WAVEGRAD: ESTIMATING GRADIENTS FOR WAVEFORM GENERATION |
d247026123 | Many Neural Network Pruning approaches consist of several iterative training and pruning steps, seemingly losing a significant amount of their performance after pruning and then recovering it in the subsequent retraining phase. Recent works of Renda et al. (2020) and Le & Hua (2021) demonstrate the significance of th... | HOW I LEARNED TO STOP WORRYING AND LOVE RETRAINING |
d48361056 | We examine two different techniques for parameter averaging in GAN training. Moving Average (MA) computes the time-average of parameters, whereas Exponential Moving Average (EMA) computes an exponentially discounted sum. Whilst MA is known to lead to convergence in bilinear settings, we provide theto our knowledge -fir... | THE UNUSUAL EFFECTIVENESS OF AVERAGING IN GAN TRAINING |
d236635303 | This paper considers two-player zero-sum finite-horizon Markov games with simultaneous moves. The study focuses on the challenging settings where the value function or the model is parameterized by general function classes. Provably efficient algorithms for both decoupled and coordinated settings are developed. In the ... | Towards General Function Approximation in Zero-Sum Markov Games |
d1803861 | We present the Neural Physics Engine (NPE), a framework for learning simulators of intuitive physics that naturally generalize across variable object count and different scene configurations. We propose a factorization of a physical scene into composable object-based representations and a neural network architecture wh... | A COMPOSITIONAL OBJECT-BASED APPROACH TO LEARNING PHYSICAL DYNAMICS |
d231719730 | We propose the task of disambiguating symbolic expressions in informal STEM documents in the form of L A T E X files -that is, determining their precise semantics and abstract syntax tree -as a neural machine translation task. We discuss the distinct challenges involved and present a dataset with roughly 33,000 entries... | DISAMBIGUATING SYMBOLIC EXPRESSIONS IN INFORMAL DOCUMENTS |
d258947377 | Whitening loss provides theoretical guarantee in avoiding feature collapse for self-supervised learning (SSL) using joint embedding architectures.One typical implementation of whitening loss is hard whitening that designs whitening transformation over embedding and imposes the loss on the whitened output.In this paper,... | Modulate Your Spectrum in Self-Supervised Learning |
d58014184 | Neural Processes (NPs)(Garnelo et al., 2018a;b)approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each function models the distribution of the output given an input, conditioned on the context. NPs have the benefit of fitting observed data ef... | ATTENTIVE NEURAL PROCESSES |
d249538415 | Construction of a scaffold structure that supports a desired motif, conferring protein function, shows promise for the design of vaccines and enzymes. But a general solution to this motif-scaffolding problem remains open. Current machine-learning techniques for scaffold design are either limited to unrealistically smal... | DIFFUSION PROBABILISTIC MODELING OF PROTEIN BACKBONES IN 3D FOR THE MOTIF-SCAFFOLDING PROBLEM |
d1248661 | Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to make errors during prediction. To better understand such attacks, a characterization is needed of the properties of regions (the so-called 'adversarial subsp... | CHARACTERIZING ADVERSARIAL SUBSPACES USING LOCAL INTRINSIC DIMENSIONALITY |
d3051911 | We describe a neural attention model with a learnable retinal sampling lattice. The model is trained on a visual search task requiring the classification of an object embedded in a visual scene amidst background distractors using the smallest number of fixations. We explore the tiling properties that emerge in the mode... | EMERGENCE OF FOVEAL IMAGE SAMPLING FROM LEARNING TO ATTEND IN VISUAL SCENES |
d252968170 | While large-scale sequence modeling from offline data has led to impressive performance gains in natural language and image generation, directly translating such ideas to robotics has been challenging. One critical reason for this is that uncurated robot demonstration data, i.e. play data, collected from non-expert hum... | FROM PLAY TO POLICY: CONDITIONAL BEHAVIOR GENERATION FROM UNCURATED ROBOT DATA |
d264406180 | Understanding the inner workings of machine learning models like Transformers is vital for their safe and ethical use.This paper presents an in-depth analysis of a one-layer Transformer model trained for integer addition.We reveal that the model divides the task into parallel, digit-specific streams and employs distinc... | Understanding Addition in Transformers |
d59413817 | Recurrent Neural Networks (RNNs) are very successful at solving challenging problems with sequential data. However, this observed efficiency is not yet entirely explained by theory. It is known that a certain class of multiplicative RNNs enjoys the property of depth efficiency -a shallow network of exponentially large ... | GENERALIZED TENSOR MODELS FOR RECURRENT NEURAL NETWORKS |
d256826752 | We introduce the use of generative adversarial learning to compute equilibria in general gametheoretic settings, specifically the generalized Nash equilibrium (GNE) in pseudo-games, and its specific instantiation as the competitive equilibrium (CE) in Arrow-Debreu competitive economies. Pseudo-games are a generalizatio... | GENERATIVE ADVERSARIAL EQUILIBRIUM SOLVERS |
d238419359 | Question Answering (QA) has been a long-standing research topic in AI and NLP fields, and a wealth of studies have been conducted to attempt to equip QA systems with human-level reasoning capability. To approximate the complicated human reasoning process, state-of-the-art QA systems commonly use pre-trained language mo... | GNN IS A COUNTER? REVISITING GNN FOR QUESTION ANSWERING |
d264306063 | Widely used language models (LMs) are typically built by scaling up a two-stage training pipeline: a pretraining stage that uses a very large, diverse dataset of text and a fine-tuning (sometimes, 'alignment') stage that uses targeted examples or other specifications of desired behaviors.While it has been hypothesized ... | An Emulator for Fine-Tuning Large Language Models using Small Language Models |
d244714829 | We propose a novel scene representation that encodes reaching distance -the distance between any position in the scene to a goal along a feasible trajectory. We demonstrate that this environment field representation can directly guide the dynamic behaviors of agents in 2D mazes or 3D indoor scenes. Our environment fiel... | LEARNING CONTINUOUS ENVIRONMENT FIELDS VIA IMPLICIT FUNCTIONS |
d253510295 | We propose a novel edge guided generative adversarial network with contrastive learning (ECGAN) for the challenging semantic image synthesis task. Although considerable improvement has been achieved, the quality of synthesized images is far from satisfactory due to three largely unresolved challenges. 1) The semantic l... | EDGE GUIDED GANS WITH CONTRASTIVE LEARNING FOR SEMANTIC IMAGE SYNTHESIS |
d252873224 | The implicit biases of gradient-based optimization algorithms are conjectured to be a major factor in the success of modern deep learning. In this work, we investigate the implicit bias of gradient flow and gradient descent in two-layer fully-connected neural networks with leaky ReLU activations when the training data ... | Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data |
d14298291 | Combining abstract, symbolic reasoning with continuous neural reasoning is a grand challenge of representation learning. As a step in this direction, we propose a new architecture, called neural equivalence networks, for the problem of learning continuous semantic representations of algebraic and logical expressions. T... | Learning Continuous Semantic Representations of Symbolic Expressions |
d228705808 | Exploratory analysis of time series data can yield a better understanding of complex dynamical systems. Granger causality is a practical framework for analysing interactions in sequential data, applied in a wide range of domains. In this paper, we propose a novel framework for inferring multivariate Granger causality u... | INTERPRETABLE MODELS FOR GRANGER CAUSALITY USING SELF-EXPLAINING NEURAL NETWORKS |
d49428777 | Neuronal assemblies, loosely defined as subsets of neurons with reoccurring spatiotemporally coordinated activation patterns, or "motifs", are thought to be building blocks of neural representations and information processing. We here propose LeMoNADe, a new exploratory data analysis method that facilitates hunting for... | LEMONADE: LEARNED MOTIF AND NEURONAL ASSEMBLY DETECTION IN CALCIUM IMAGING VIDEOS |
d256846551 | We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark. Unlike most existing question-answering (QA) datasets, we expect models to not only answer questions, but also produce step-by-step structured explanations describing how premises in the question are used to... | STREET: A MULTI-TASK STRUCTURED REASONING AND EXPLANATION BENCHMARK |
d252683429 | We present TabPFN, a trained Transformer that can do supervised classification for small tabular datasets in less than a second, needs no hyperparameter tuning and is competitive with state-of-the-art classification methods. TabPFN performs in-context learning (ICL), it learns to make predictions using sequences of lab... | TABPFN: A TRANSFORMER THAT SOLVES SMALL TABULAR CLASSIFICATION PROBLEMS IN A SECOND |
d53094405 | Generating musical audio directly with neural networks is notoriously difficult because it requires coherently modeling structure at many different timescales. Fortunately, most music is also highly structured and can be represented as discrete note events played on musical instruments. Herein, we show that by using no... | ENABLING FACTORIZED PIANO MUSIC MODELING AND GENERATION WITH THE MAESTRO DATASET |
d227127234 | We present a hierarchical VAE that, for the first time, outperforms the PixelCNN in log-likelihood on all natural image benchmarks. We begin by observing that VAEs can actually implement autoregressive models, and other, more efficient generative models, if made sufficiently deep. Despite this, autoregressive models ha... | VERY DEEP VAES GENERALIZE AUTOREGRESSIVE MODELS AND CAN OUTPERFORM THEM ON IMAGES |
d252693111 | Recently, Transformer-based image restoration networks have achieved promising improvements over convolutional neural networks due to parameter-independent global interactions. To lower computational cost, existing works generally limit self-attention computation within non-overlapping windows. However, each group of t... | ACCURATE IMAGE RESTORATION WITH ATTENTION RETRACTABLE TRANSFORMER |
d211068821 | Training machine learning models that can learn complex spatiotemporal dynamics and generalize under distributional shift is a fundamental challenge. The symmetries in a physical system play a unique role in characterizing unchanged features under transformation. We propose a systematic approach to improve generalizati... | Incorporating Symmetry into Deep Dynamics Models for Improved Generalization |
d3509777 | It is well-known that neural networks are universal approximators, but that deeper networks tend to be much more efficient than shallow ones. We shed light on this by proving that the total number of neurons m required to approximate natural classes of multivariate polynomials of n variables grows only linearly with n ... | The power of deeper networks for expressing natural functions |
d247447287 | Humans show language-biased image recognition for a word-embedded image, known as picture-word interference. Such interference depends on hierarchical semantic categories and reflects that human language processing highly interacts with visual processing. Similar to humans, recent artificial models jointly trained on t... | LANGUAGE-BIASED IMAGE CLASSIFICATION: EVALUATION BASED ON SEMANTIC REPRESENTATIONS |
d52944914 | Deep neural networks, in particular convolutional neural networks, have become highly effective tools for compressing images and solving inverse problems including denoising, inpainting, and reconstruction from few and noisy measurements. This success can be attributed in part to their ability to represent and generate... | Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks |
d254044229 | Understanding self-supervised learning is important but challenging. Previous theoretical works study the role of pretraining losses, and view neural networks as general black boxes. However, the recent work ofSaunshi et al. (2022)argues that the model architecture -a component largely ignored by previous worksalso has... | A Theoretical Study of Inductive Biases in Contrastive Learning |
d249461537 | This paper presents a method to build explicit tensor-train (TT) representations. We show that a wide class of tensors can be explicitly represented with sparse TT-cores, obtaining, in many cases, optimal TT-ranks. Numerical experiments show that our method outperforms the existing ones in several practical application... | Constructive TT-representation of the tensors given as index interaction functions with applications |
d57189428 | The high computational and parameter complexity of neural networks makes their training very slow and difficult to deploy on energy and storage-constrained computing systems. Many network complexity reduction techniques have been proposed including fixed-point implementation. However, a systematic approach for designin... | PER-TENSOR FIXED-POINT QUANTIZATION OF THE BACK-PROPAGATION ALGORITHM |
d252872923 | Devising a fair classifier that does not discriminate against different groups is an important problem in machine learning. Recently, effort-based fairness notions are getting attention, which considers the scenarios of each individual making effort to improve its feature over time. Such scenarios happen in the real wo... | EQUAL IMPROVABILITY: A NEW FAIRNESS NOTION CONSIDERING THE LONG-TERM IMPACT |
d231648391 | Knowledge about the locations of keypoints of an object in an image can assist in fine-grained classification and identification tasks, particularly for the case of objects that exhibit large variations in poses that greatly influence their visual appearance, such as wild animals. However, supervised training of a keyp... | SEMI-SUPERVISED KEYPOINT LOCALIZATION |
d58028743 | Within many machine learning algorithms, a fundamental problem concerns efficient calculation of an unbiased gradient wrt parameters γ for expectation-based objectives E qγ (y) [f (y)]. Most existing methods either (i) suffer from high variance, seeking help from (often) complicated variance-reduction techniques; or (i... | GO GRADIENT FOR EXPECTATION-BASED OBJECTIVES |
d207878944 | As machine learning methods see greater adoption and implementation in high stakes applications such as medical image diagnosis, the need for model interpretability and explanation has become more critical.Classical approaches that assess feature importance (e.g., saliency maps) do not explain how and why a particular ... | EXPLANATION BY PROGRESSIVE EXAGGERATION |
d252683376 | While the maximum entropy (MaxEnt) reinforcement learning (RL) frameworkoften touted for its exploration and robustness capabilities-is usually motivated from a probabilistic perspective, the use of deep probabilistic models has not gained much traction in practice due to their inherent complexity. In this work, we pro... | LATENT STATE MARGINALIZATION AS A LOW-COST APPROACH FOR IMPROVING EXPLORATION |
d15816492 | We formulate sequence to sequence transduction as a noisy channel decoding problem and use recurrent neural networks to parameterise the source and channel models. Unlike direct models which can suffer from explaining-away effects during training, noisy channel models must produce outputs that explain their inputs, and... | THE NEURAL NOISY CHANNEL |
d265038424 | Generative Adversarial Networks (GANs) are one of the most popular tools for learning complex high dimensional distributions. However, generalization properties of GANs have not been well understood. In this paper, we analyze the generalization of GANs in practical settings. We show that discriminators trained on discr... | IMPROVING GENERALIZATION AND STABILITY OF GENERATIVE ADVERSARIAL NETWORKS |
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