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d259316542 | Recently, a series of papers proposed deep learning-based approaches to sample from unnormalized target densities using controlled diffusion processes. In this work, we identify these approaches as special cases of the Schrödinger bridge problem, seeking the most likely stochastic evolution between a given prior distri... | Improved sampling via learned diffusions |
d252595603 | Acquiring labeled data is challenging in many machine learning applications with limited budgets. Active learning gives a procedure to select the most informative data points and improve data efficiency by reducing the cost of labeling. The infomax learning principle maximizing mutual information such as BALD has been ... | ACTIVE LEARNING IN BAYESIAN NEURAL NETWORKS WITH BALANCED ENTROPY LEARNING PRINCIPLE |
d222133333 | Multitask Reinforcement Learning is a promising way to obtain models with better performance, generalisation, data efficiency, and robustness. Most existing work is limited to compatible settings, where the state and action space dimensions are the same across tasks. Graph Neural Networks (GNN) are one way to address i... | MY BODY IS A CAGE: THE ROLE OF MORPHOLOGY IN GRAPH-BASED INCOMPATIBLE CONTROL |
d252715459 | We propose topology-aware feature partitioning into k disjoint partitions for given scene features as a method for object-centric representation learning.To this end, we propose to use minimum s-t graph cuts as a partitioning method which is represented as a linear program.The method is topologically aware since it exp... | DIFFERENTIABLE MATHEMATICAL PROGRAMMING FOR OBJECT-CENTRIC REPRESENTATION LEARNING |
d249538584 | Reinforcement Learning (RL) methods are typically applied directly in environments to learn policies. In some complex environments with continuous state-action spaces, sparse rewards, and/or long temporal horizons, learning a good policy in the original environments can be difficult. Focusing on the offline RL setting,... | VALUE MEMORY GRAPH: A GRAPH-STRUCTURED WORLD MODEL FOR OFFLINE REINFORCEMENT LEARNING |
d256662277 | Denoising diffusion models have spurred significant gains in density modeling and image generation, precipitating an industrial revolution in text-guided AI art generation. We introduce a new mathematical foundation for diffusion models inspired by classic results in information theory that connect Information with Min... | INFORMATION-THEORETIC DIFFUSION |
d248228159 | Building sample-efficient agents that generalize out-of-distribution (OOD) in realworld settings remains a fundamental unsolved problem on the path towards achieving higher-level cognition. One particularly promising approach is to begin with low-dimensional, pretrained representations of our world, which should facili... | THE ROLE OF PRETRAINED REPRESENTATIONS FOR THE OOD GENERALIZATION OF RL AGENTS |
d221879071 | Recent network pruning methods focus on pruning models early-on in training. To estimate the impact of removing a parameter, these methods use importance measures that were originally designed to prune trained models. Despite lacking justification for their use early-on in training, such measures result in surprisingly... | A GRADIENT FLOW FRAMEWORK FOR ANALYZING NETWORK PRUNING |
d264436558 | The inductive bias of a graph neural network (GNN) is largely encoded in its specified graph.Latent graph inference relies on latent geometric representations to dynamically rewire or infer a GNN's graph to maximize the GNN's predictive downstream performance, but it lacks solid theoretical foundations in terms of embe... | NEURAL SNOWFLAKES: UNIVERSAL LATENT GRAPH INFERENCE VIA TRAINABLE LATENT GEOMETRIES |
d9226593 | Visual servoing involves choosing actions that move a robot in response to observations from a camera, in order to reach a goal configuration in the world. Standard visual servoing approaches typically rely on manually designed features and analytical dynamics models, which limits their generalization capability and of... | LEARNING VISUAL SERVOING WITH DEEP FEATURES AND FITTED Q-ITERATION |
d236087592 | An important component for generalization in machine learning is to uncover underlying latent factors of variation as well as the mechanism through which each factor acts in the world. In this paper, we test whether 17 unsupervised, weakly supervised, and fully supervised representation learning approaches correctly in... | VISUAL REPRESENTATION LEARNING DOES NOT GENERALIZE STRONGLY WITHIN THE SAME DOMAIN |
d211818298 | Class selectivity-typically defined as how different a neuron's responses are across different classes of stimuli or data samples-is a common metric used to interpret the function of individual neurons in biological and artificial neural networks. However, it remains an open question whether it is necessary and/or suff... | Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs |
d259164552 | Due to the limited scale and quality of video-text training corpus, most visionlanguage foundation models employ image-text datasets for pretraining and primarily focus on modeling visually semantic representations while disregarding temporal semantic representations and correlations. To address this issue, we propose ... | COSA: Concatenated Sample Pretrained Vision-Language Foundation Model |
d247315014 | Fully exploiting the learning capacity of neural networks requires overparameterized dense networks. On the other side, directly training sparse neural networks typically results in unsatisfactory performance. Lottery Ticket Hypothesis (LTH) provides a novel view to investigate sparse network training and maintain its ... | DUAL LOTTERY TICKET HYPOTHESIS |
d52909749 | We present Optimal Completion Distillation (OCD), a training procedure for optimizing sequence to sequence models based on edit distance. OCD is efficient, has no hyper-parameters of its own, and does not require pretraining or joint optimization with conditional log-likelihood. Given a partial sequence generated by th... | OPTIMAL COMPLETION DISTILLATION FOR SEQUENCE LEARNING |
d252544861 | Sliced Wasserstein (SW) distance has been widely used in different application scenarios since it can be scaled to a large number of supports without suffering from the curse of dimensionality. The value of sliced Wasserstein distance is the average of transportation cost between one-dimensional representations (projec... | HIERARCHICAL SLICED WASSERSTEIN DISTANCE |
d53535787 | Convolutional architectures have recently been shown to be competitive on many sequence modelling tasks when compared to the de-facto standard of recurrent neural networks (RNNs), while providing computational and modeling advantages due to inherent parallelism. However, currently there remains a performance gap to mor... | STCN: STOCHASTIC TEMPORAL CONVOLUTIONAL NETWORKS |
d209485573 | Recent powerful pre-trained language models have achieved remarkable performance on most of the popular datasets for reading comprehension. It is time to introduce more challenging datasets to push the development of this field towards more comprehensive reasoning of text. In this paper, we introduce a new Reading Comp... | RECLOR: A READING COMPREHENSION DATASET REQUIRING LOGICAL REASONING |
d259203115 | As their size increases, Large Languages Models (LLMs) are natural candidates for network pruning methods: approaches that drop a subset of network weights while striving to preserve performance.Existing methods, however, require either retraining, which is rarely affordable for billion-scale LLMs, or solving a weight ... | A SIMPLE AND EFFECTIVE PRUNING APPROACH FOR LARGE LANGUAGE MODELS |
d257365071 | An energy-based model (EBM) is a popular generative framework that offers both explicit density and architectural flexibility, but training them is difficult since it is often unstable and time-consuming.In recent years, various training techniques have been developed, e.g., better divergence measures or stabilization ... | GUIDING ENERGY-BASED MODELS VIA CONTRASTIVE LATENT VARIABLES |
d213513188 | Generative adversarial networks (GANs) are one of the most popular approaches when it comes to training generative models, among which variants of Wasserstein GANs are considered superior to the standard GAN formulation in terms of learning stability and sample quality. However, Wasserstein GANs require the critic to b... | ADVERSARIAL LIPSCHITZ REGULARIZATION |
d247451183 | Reliable out-of-distribution (OOD) detection is fundamental to implementing safer modern machine learning (ML) systems. In this paper, we introduce IGEOOD, an effective method for detecting OOD samples. IGEOOD applies to any pre-trained neural network, works under various degrees of access to the ML model, does not req... | IGEOOD: AN INFORMATION GEOMETRY APPROACH TO OUT-OF-DISTRIBUTION DETECTION |
d263622464 | Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their rewards.GFlowNets exhibit a remarkable ability to generate diverse samples, yet occasionally struggle to consistently produce samples with high rewards due to over-exploration on wid... | LOCAL SEARCH GFLOWNETS |
d247476432 | Learning energy-based model (EBM) requires MCMC sampling of the learned model as an inner loop of the learning algorithm. However, MCMC sampling of EBMs in high-dimensional data space is generally not mixing, because the energy function, which is usually parametrized by a deep network, is highly multi-modal in the data... | MCMC SHOULD MIX: LEARNING ENERGY-BASED MODEL WITH NEURAL TRANSPORT LATENT SPACE MCMC |
d260704723 | Recent advancements in Multimodal Large Language Models (MLLMs) have been utilizing Visual Prompt Generators (VPGs) to convert visual features into tokens that LLMs can recognize. This is achieved by training the VPGs on millions of image-caption pairs, where the VPG-generated tokens of images are fed into a frozen LLM... | FINE-TUNING MULTIMODAL LLMS TO FOLLOW ZERO-SHOT DEMONSTRATIVE INSTRUCTIONS |
d249191923 | The well-designed structures in neural networks reflect the prior knowledge incorporated into the models. However, though different models have various priors, we are used to training them with model-agnostic optimizers such as SGD. In this paper, we propose to incorporate model-specific prior knowledge into optimizers... | RE-PARAMETERIZING YOUR OPTIMIZERS RATHER THAN ARCHITECTURES |
d54083992 | Many real-world systems studied are governed by complex, nonlinear dynamics. By modeling these dynamics, we can gain insight into how these systems work, make predictions about how they will behave, and develop strategies for controlling them. While there are many methods for modeling nonlinear dynamical systems, exist... | TREE-STRUCTURED RECURRENT SWITCHING LINEAR DYNAMICAL SYSTEMS FOR MULTI-SCALE MODELING |
d251765079 | Generating new molecules with specified chemical and biological properties via generative models has emerged as a promising direction for drug discovery. However, existing methods require extensive training/fine-tuning with a large dataset, often unavailable in real-world generation tasks. In this work, we propose a ne... | RETRIEVAL-BASED CONTROLLABLE MOLECULE GENERATION |
d257219652 | Graph unlearning, which involves deleting graph elements such as nodes, node labels, and relationships from a trained graph neural network (GNN) model, is crucial for real-world applications where data elements may become irrelevant, inaccurate, or privacy-sensitive. However, existing methods for graph unlearning eithe... | GNNDELETE: A GENERAL STRATEGY FOR UNLEARNING IN GRAPH NEURAL NETWORKS |
d250264425 | The vanishing ideal of a set of points = {x 1 , . . . , x } ⊆ ℝ is the set of polynomials that evaluate to 0 over all points x ∈ and admits an efficient representation by a finite subset of generators. In practice, to accommodate noise in the data, algorithms that construct generators of the approximate vanishing ideal... | Approximate Vanishing Ideal Computations at Scale |
d108300573 | In this paper, we present a new deep learning architecture for addressing the problem of supervised learning with sparse and irregularly sampled multivariate time series. The architecture is based on the use of a semi-parametric interpolation network followed by the application of a prediction network. The interpolatio... | INTERPOLATION-PREDICTION NETWORKS FOR IRREGULARLY SAMPLED TIME SERIES |
d8394195 | We propose a new approach to the problem of optimizing autoencoders for lossy image compression. New media formats, changing hardware technology, as well as diverse requirements and content types create a need for compression algorithms which are more flexible than existing codecs. Autoencoders have the potential to ad... | LOSSY IMAGE COMPRESSION WITH COMPRESSIVE AUTOENCODERS |
d239016943 | Inspired by the notion that "to copy is easier than to memorize", in this work, we introduce GNN-LM, which extends vanilla neural language model (LM) by allowing to reference similar contexts in the entire training corpus. We build a directed heterogeneous graph between an input context and its semantically related nei... | GNN-LM: LANGUAGE MODELING BASED ON GLOBAL CONTEXTS VIA GNN |
d12713052 | Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and trai... | NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING |
d263609190 | State-space models (SSMs) have recently emerged as a framework for learning long-range sequence tasks.An example is the structured state-space sequence (S4) layer, which uses the diagonal-plus-low-rank structure of the HiPPO initialization framework.However, the complicated structure of the S4 layer poses challenges; a... | Robustifying State-space Models for Long Sequences via Approximate Diagonalization |
d232257635 | Recently, Frankle & Carbin(2019)demonstrated that randomly-initialized dense networks contain subnetworks that once found can be trained to reach test accuracy comparable to the trained dense network. However, finding these high performing trainable subnetworks is expensive, requiring iterative process of training and ... | Multi-Prize Lottery Ticket Hypothesis: FINDING ACCURATE BINARY NEURAL NETWORKS BY PRUNING A RANDOMLY WEIGHTED NETWORK |
d3538865 | Sequence-to-sequence models with soft attention have been successfully applied to a wide variety of problems, but their decoding process incurs a quadratic time and space cost and is inapplicable to real-time sequence transduction. To address these issues, we propose Monotonic Chunkwise Attention (MoChA), which adaptiv... | MONOTONIC CHUNKWISE ATTENTION |
d254125312 | Privacy noise may negate the benefits of using adaptive optimizers in differentially private model training. Prior works typically address this issue by using auxiliary information (e.g., public data) to boost the effectiveness of adaptive optimization. In this work, we explore techniques to estimate and efficiently ad... | DIFFERENTIALLY PRIVATE ADAPTIVE OPTIMIZATION WITH DELAYED PRECONDITIONERS |
d227746078 | The task of information retrieval is an important component of many natural language processing systems, such as open domain question answering. While traditional methods were based on hand-crafted features, continuous representations based on neural networks recently obtained competitive results. A challenge of using ... | DISTILLING KNOWLEDGE FROM READER TO RETRIEVER FOR QUESTION ANSWERING |
d222177403 | Network binarization is a promising hardware-aware direction for creating efficient deep models. Despite its memory and computational advantages, reducing the accuracy gap between such models and their real-valued counterparts remains an unsolved challenging research problem. To this end, we make the following 3 contri... | HIGH-CAPACITY EXPERT BINARY NETWORKS |
d211146411 | Knowledge-grounded dialogue is a task of generating an informative response based on both discourse context and external knowledge. As we focus on better modeling the knowledge selection in the multi-turn knowledge-grounded dialogue, we propose a sequential latent variable model as the first approach to this matter. Th... | SEQUENTIAL LATENT KNOWLEDGE SELECTION FOR KNOWLEDGE-GROUNDED DIALOGUE |
d3571422 | Generative adversarial networks (GANs) form a generative modeling approach known for producing appealing samples, but they are notably difficult to train. One common way to tackle this issue has been to propose new formulations of the GAN objective. Yet, surprisingly few studies have looked at optimization methods desi... | A VARIATIONAL INEQUALITY PERSPECTIVE ON GENERATIVE ADVERSARIAL NETWORKS |
d3285020 | Existing deep multitask learning (MTL) approaches align layers shared between tasks in a parallel ordering. Such an organization significantly constricts the types of shared structure that can be learned. The necessity of parallel ordering for deep MTL is first tested by comparing it with permuted ordering of shared la... | BEYOND SHARED HIERARCHIES: DEEP MULTITASK LEARNING THROUGH SOFT LAYER ORDERING |
d3700344 | We frame Question Answering as a Reinforcement Learning task, an approach that we call Active Question Answering. We propose an agent that sits between the user and a black box question-answering system an which learns to reformulate questions to elicit the best possible answers.The agent probes the system with, potent... | Ask the Right Questions: Active Question Reformulation with Reinforcement Learning |
d250451329 | In this paper we study online Reinforcement Learning (RL) in partially observable dynamical systems. We focus on the Predictive State Representations (PSRs) model, which is an expressive model that captures other well-known models such as Partially Observable Markov Decision Processes (POMDP). PSR represents the states... | PAC Reinforcement Learning for Predictive State Representations |
d259203617 | Congestion is a common failure mode of markets, where consumers compete inefficiently on the same subset of goods (e.g., chasing the same small set of properties on a vacation rental platform). The typical economic story is that prices solve this problem by balancing supply and demand in order to decongest the market. ... | Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces |
d32654687 | DeConvNet, Guided BackProp, LRP, were invented to better understand deep neural networks. We show that these methods do not produce the theoretically correct explanation for a linear model. Yet they are used on multi-layer networks with millions of parameters. This is a cause for concern since linear models are simple ... | LEARNING HOW TO EXPLAIN NEURAL NETWORKS: PATTERNNET AND PATTERNATTRIBUTION |
d209315300 | Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences. We introduce two techniques to improve the efficiency of Transformers. For one, we replace dot-product attention by one that uses locality-sensiti... | REFORMER: THE EFFICIENT TRANSFORMER |
d264172720 | In this paper, we present TOSS, which introduces text to the task of novel view synthesis (NVS) from just a single RGB image. While Zero123 has demonstrated impressive zero-shot open-set NVS capability, it treats NVS as a pure image-toimage translation problem. This approach suffers from the challengingly underconstrai... | TOSS: HIGH-QUALITY TEXT-GUIDED NOVEL VIEW SYNTHESIS FROM A SINGLE IMAGE |
d238744320 | Semi-supervised learning (SSL) has demonstrated its potential to improve the model accuracy for a variety of learning tasks when the high-quality supervised data is severely limited. Although it is often established that the average accuracy for the entire population of data is improved, it is unclear how SSL fares wit... | THE RICH GET RICHER: DISPARATE IMPACT OF SEMI-SUPERVISED LEARNING |
d252846166 | Discovering causal relations from observational data becomes possible with additional assumptions such as considering the functional relations to be constrained as nonlinear with additive noise (ANM). Even with strong assumptions, causal discovery involves an expensive search problem over the space of directed acyclic ... | DIFFUSION MODELS FOR CAUSAL DISCOVERY VIA TOPOLOGICAL ORDERING |
d3947794 | Deep neural network classifiers are vulnerable to small input perturbations carefully generated by the adversaries. Injecting adversarial inputs during training, known as adversarial training, can improve robustness against one-step attacks, but not for unknown iterative attacks. To address this challenge, we propose t... | Cascade Adversarial Machine Learning Regularized with a Unified Embedding |
d257532315 | Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and v... | POLICY PRE-TRAINING FOR AUTONOMOUS DRIVING VIA SELF-SUPERVISED GEOMETRIC MODELING |
d252917826 | Proteins are macromolecules that perform essential functions in all living organisms.Designing novel proteins with specific structures and desired functions has been a long-standing challenge in the field of bioengineering.Existing approaches generate both protein sequence and structure using either autoregressive mode... | PROTEIN SEQUENCE AND STRUCTURE CO-DESIGN WITH EQUIVARIANT TRANSLATION |
d263606194 | In this work, we consider the optimization process of minibatch stochastic gradient descent (SGD) on a 2-layer neural network with data separated by a quadratic ground truth function. We prove that with data drawn from the d-dimensional Boolean hypercube labeled by the quadratic "XOR" function y = −xixj, it is possible... | SGD Finds then Tunes Features in Two-Layer Neural Networks with Near-Optimal Sample Complexity: A Case Study in the XOR problem |
d215737267 | This work presents a modular and hierarchical approach to learn policies for exploring 3D environments, called 'Active Neural SLAM'. Our approach leverages the strengths of both classical and learning-based methods, by using analytical path planners with learned SLAM module, and global and local policies. The use of le... | LEARNING TO EXPLORE USING ACTIVE NEURAL SLAM |
d11245315 | Dropout, a simple and effective way to train deep neural networks, has led to a number of impressive empirical successes and spawned many recent theoretical investigations. However, the gap between dropout's training and inference phases, introduced due to tractability considerations, has largely remained under-appreci... | DROPOUT WITH EXPECTATION-LINEAR REGULARIZATION |
d238419331 | Current dense text retrieval models face two typical challenges. First, they adopt a siamese dual-encoder architecture to encode queries and documents independently for fast indexing and searching, while neglecting the finer-grained termwise interactions. This results in a sub-optimal recall performance. Second, their ... | ADVERSARIAL RETRIEVER-RANKER FOR DENSE TEXT RETRIEVAL |
d119304640 | Figure 1. Our method extracts a character from an uncontrolled video and enables us to control its motion. The pose of the character, shown in the first row, is created by our Pose2Pose network in an autoregressive way, so that the motion matches the control signal illustrated by the joystick. The second row depicts th... | Vid2Game: Controllable Characters Extracted from Real-World Videos |
d223953594 | In recent deep image compression neural networks, the entropy model plays a critical role in estimating the prior distribution of deep image encodings. Existing methods combine hyperprior with local context in the entropy estimation function. This greatly limits their performance due to the absence of a global vision. ... | LEARNING ACCURATE ENTROPY MODEL WITH GLOBAL REFERENCE FOR IMAGE COMPRESSION |
d252907682 | A successful paradigm in representation learning is to perform self-supervised pretraining using tasks based on mini-batch statistics (e.g., SimCLR, VICReg, SwAV, MSN). We show that in the formulation of all these methods is an overlooked prior to learn features that enable uniform clustering of the data. While this pr... | THE HIDDEN UNIFORM CLUSTER PRIOR IN SELF-SUPERVISED LEARNING |
d600040 | Program translation is an important tool to migrate legacy code in one language into an ecosystem built in a different language. In this work, we are the first to employ deep neural networks toward tackling this problem. We observe that program translation is a modular procedure, in which a sub-tree of the source tree ... | Tree-to-tree Neural Networks for Program Translation |
d219708801 | Traditional off-policy actor-critic Reinforcement Learning (RL) algorithms learn value functions of a single target policy. However, when value functions are updated to track the learned policy, they forget potentially useful information about old policies. We introduce a class of value functions called Parameter-Based... | PARAMETER-BASED VALUE FUNCTIONS |
d259887064 | Overconfidence is a common issue for deep neural networks, limiting their deployment in real-world applications. To better estimate confidence, existing methods mostly focus on fully-supervised scenarios and rely on training labels. In this paper, we propose the first confidence estimation method for a semi-supervised ... | CONFIDENCE ESTIMATION USING UNLABELED DATA |
d256697328 | This work aims to tackle a major challenge in offline Inverse Reinforcement Learning (IRL), namely the reward extrapolation error, where the learned reward function may fail to explain the task correctly and misguide the agent in unseen environments due to the intrinsic covariate shift. Leveraging both expert data and ... | CLARE: CONSERVATIVE MODEL-BASED REWARD LEARNING FOR OFFLINE INVERSE REINFORCEMENT LEARNING |
d257078858 | Intelligent systems deployed in the real world suffer from catastrophic forgetting when exposed to a sequence of tasks. Humans, on the other hand, acquire, consolidate, and transfer knowledge between tasks that rarely interfere with the consolidated knowledge. Accompanied by self-regulated neurogenesis, continual learn... | TASK-AWARE INFORMATION ROUTING FROM COMMON REPRESENTATION SPACE IN LIFELONG LEARNING |
d245005650 | Despite overparameterization, deep networks trained via supervised learning are easy to optimize and exhibit excellent generalization. One hypothesis to explain this is that overparameterized deep networks enjoy the benefits of implicit regularization induced by stochastic gradient descent, which favors parsimonious so... | DR3: VALUE-BASED DEEP REINFORCEMENT LEARNING REQUIRES EXPLICIT REGULARIZATION |
d263311025 | Pretrained language models sometimes possess knowledge that we do not wish them to, including memorized personal information and knowledge that could be used to harm people. They can also output toxic or harmful text. To mitigate these safety and informational issues, we propose an attack-and-defense framework for stud... | CAN SENSITIVE INFORMATION BE DELETED FROM LLMS? OBJECTIVES FOR DEFENDING AGAINST EXTRACTION ATTACKS |
d258437253 | We introduce Robust Exploration via Clusteringbased Online Density Estimation (RECODE), a nonparametric method for novelty-based exploration that estimates visitation counts for clusters of states based on their similarity in a chosen embedding space. By adapting classical clustering to the nonstationary setting of Dee... | Unlocking the Power of Representations in Long-term Novelty-based Exploration |
d264172845 | Invariance and equivariance to geometrical transformations have proven to be very useful inductive biases when training (convolutional) neural network models, especially in the low-data regime. Much work has focused on the case where the symmetry group employed is compact or abelian, or both. Recent work has explored e... | LIE GROUP DECOMPOSITIONS FOR EQUIVARIANT NEURAL NETWORKS |
d260866107 | We present a scalable method to build a high quality instruction following language model by automatically labelling human-written text with corresponding instructions. Our approach, named instruction backtranslation, starts with a language model finetuned on a small amount of seed data, and a given web corpus. The see... | Self-Alignment with Instruction Backtranslation |
d53483457 | Memory-augmented neural networks consisting of a neural controller and an external memory have shown potentials in long-term sequential learning. Current RAM-like memory models maintain memory accessing every timesteps, thus they do not effectively leverage the short-term memory held in the controller. We hypothesize t... | Learning to Remember More with Less Memorization |
d238634219 | Differentially Private (DP) learning has seen limited success for building large deep learning models of text, and straightforward attempts at applying Differentially Private Stochastic Gradient Descent (DP-SGD) to NLP tasks have resulted in large performance drops and high computational overhead. We show that this per... | LARGE LANGUAGE MODELS CAN BE STRONG DIFFERENTIALLY PRIVATE LEARNERS |
d264306111 | This paper reveals that large language models (LLMs), despite being trained solely on textual data, are surprisingly strong encoders for purely visual tasks in the absence of language.Even more intriguingly, this can be achieved by a simple yet previously overlooked strategy -employing a frozen transformer block from p... | FROZEN TRANSFORMERS IN LANGUAGE MODELS ARE EFFECTIVE VISUAL ENCODER LAYERS |
d257232328 | The success of machine learning relies heavily on massive amounts of data, which are usually generated and stored across a range of diverse and distributed data sources.Decentralized learning has thus been advocated and widely deployed to make efficient use of the distributed datasets, with an extensive focus on superv... | DOES LEARNING FROM DECENTRALIZED NON-IID UNLABELED DATA BENEFIT FROM SELF SUPERVISION? |
d235313931 | Policy-Space Response Oracles (PSRO) is a general algorithmic framework for learning policies in multiagent systems by interleaving empirical game analysis with deep reinforcement learning (Deep RL). At each iteration, Deep RL is invoked to train a best response to a mixture of opponent policies. The repeated applicati... | ITERATIVE EMPIRICAL GAME SOLVING VIA SINGLE POLICY BEST RESPONSE |
d202889322 | Some of the most successful applications of deep reinforcement learning to challenging domains in discrete and continuous control have used policy gradient methods in the on-policy setting. However, policy gradients can suffer from large variance that may limit performance, and in practice require carefully tuned entro... | V-MPO: ON-POLICY MAXIMUM A POSTERIORI POLICY OPTIMIZATION FOR DISCRETE AND CONTINUOUS CONTROL |
d209444850 | Adversarial examples are crafted with imperceptible perturbations with the intent to fool neural networks. Against such attacks, adversarial training and its variants stand as the strongest defense to date. Previous studies have pointed out that robust models that have undergone adversarial training tend to produce mor... | JACOBIAN ADVERSARIALLY REGULARIZED NETWORKS FOR ROBUSTNESS |
d260091821 | Despite the remarkable success of diffusion models in image generation, slow sampling remains a persistent issue. To accelerate the sampling process, prior studies have reformulated diffusion sampling as an ODE/SDE and introduced higher-order numerical methods. However, these methods often produce divergence artifacts,... | Diffusion Sampling with Momentum for Mitigating Divergence Artifacts |
d263829555 | We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control.Due to the highdimensionality of humanoid control as well as the inherent difficulties in reinforcement learning, prior methods have focused on learning skill embeddings for a narrow ra... | UNIVERSAL HUMANOID MOTION REPRESENTATIONS FOR PHYSICS-BASED CONTROL |
d249192258 | Contrastive learning, especially self-supervised contrastive learning (SSCL), has achieved great success in extracting powerful features from unlabeled data. In this work, we contribute to the theoretical understanding of SSCL and uncover its connection to the classic data visualization method, stochastic neighbor embe... | YOUR CONTRASTIVE LEARNING IS SECRETLY DOING STOCHASTIC NEIGHBOR EMBEDDING |
d252668622 | Traditional analyses of gradient descent show that when the largest eigenvalue of the Hessian, also known as the sharpness S(θ), is bounded by 2/η, training is "stable" and the training loss decreases monotonically. Recent works, however, have observed that this assumption does not hold when training modern neural netw... | Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability |
d67856213 | Efficient audio synthesis is an inherently difficult machine learning task, as human perception is sensitive to both global structure and fine-scale waveform coherence. Autoregressive models, such as WaveNet, model local structure but have slow iterative sampling and lack global latent structure. In contrast, Generativ... | GANSYNTH: ADVERSARIAL NEURAL AUDIO SYNTHESIS |
d252715491 | Time series analysis is of immense importance in extensive applications, such as weather forecasting, anomaly detection, and action recognition.This paper focuses on temporal variation modeling, which is the common key problem of extensive analysis tasks.Previous methods attempt to accomplish this directly from the 1D ... | TIMESNET: TEMPORAL 2D-VARIATION MODELING FOR GENERAL TIME SERIES ANALYSIS |
d263622302 | We rigorously study the joint evolution of training dynamics via stochastic gradient descent (SGD) and the spectra of empirical Hessian and gradient matrices. We prove that in two canonical classification tasks for multi-class high-dimensional mixtures and either 1 or 2-layer neural networks, the SGD trajectory rapidly... | HIGH-DIMENSIONAL SGD ALIGNS WITH EMERGING OUTLIER EIGENSPACES |
d12933888 | A new model for video captioning is developed, using a deep three-dimensional Convolutional Neural Network (C3D) as an encoder for videos and a Recurrent Neural Network (RNN) as a decoder for captions. We consider both "hard" and "soft" attention mechanisms, to adaptively and sequentially focus on different layers of f... | ADAPTIVE FEATURE ABSTRACTION FOR TRANSLATING VIDEO TO LANGUAGE |
d56657912 | In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, IMAGENET-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Then we propose a new dataset called IMAGENET-P which enables researc... | BENCHMARKING NEURAL NETWORK ROBUSTNESS TO COMMON CORRUPTIONS AND PERTURBATIONS |
d202573010 | We prove the precise scaling, at finite depth and width, for the mean and variance of the neural tangent kernel (NTK) in a randomly initialized ReLU network. The standard deviation is exponential in the ratio of network depth to width. Thus, even in the limit of infinite overparameterization, the NTK is not determinist... | FINITE DEPTH AND WIDTH CORRECTIONS TO THE NEURAL TANGENT KERNEL |
d264490431 | Score Distillation Sampling (SDS) has emerged as the de facto approach for textto-content generation in non-image domains.In this paper, we reexamine the SDS process and introduce a straightforward interpretation that demystifies the necessity for large Classifier-Free Guidance (CFG) scales, rooted in the distillation ... | NOISE-FREE SCORE DISTILLATION |
d6039192 | In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based language model designed to directly capture the global semantic meaning relating words in a document via latent topics. Because of their sequential nature, RNNs are good at capturing the local structure of a word sequence -both semantic and synt... | TOPICRNN: A RECURRENT NEURAL NETWORK WITH LONG-RANGE SEMANTIC DEPENDENCY |
d258309378 | The symmetric generalized eigenvalue problem (SGEP) is a fundamental concept in numerical linear algebra. It captures the solution of many classical machine learning problems such as canonical correlation analysis, independent components analysis, partial least squares, linear discriminant analysis, principal component... | THE SYMMETRIC GENERALIZED EIGENVALUE PROBLEM AS A NASH EQUILIBRIUM |
d235253907 | In this paper, we introduce Target-Aware Weighted Training (TAWT), a weighted training algorithm for cross-task learning based on minimizing a representationbased task distance between the source and target tasks. We show that TAWT is easy to implement, is computationally efficient, requires little hyperparameter tunin... | WEIGHTED TRAINING FOR CROSS-TASK LEARNING |
d3904215 | We propose a simple extension to the ReLU-family of activation functions that allows them to shift the mean activation across a layer towards zero. Combined with proper weight initialization, this alleviates the need for normalization layers. We explore the training of deep vanilla recurrent neural networks (RNNs) with... | Shifting Mean Activation Towards Zero with Bipolar Activation Functions |
d52878445 | Batch Normalization (BN) improves both convergence and generalization in training neural networks. This work understands these phenomena theoretically. We analyze BN by using a basic block of neural networks, consisting of a kernel layer, a BN layer, and a nonlinear activation function. This basic network helps us unde... | TOWARDS UNDERSTANDING REGULARIZATION IN BATCH NORMALIZATION |
d228063930 | Despite their recent successes, GAN models for semantic image synthesis still suffer from poor image quality when trained with only adversarial supervision. Historically, additionally employing the VGG-based perceptual loss has helped to overcome this issue, significantly improving the synthesis quality, but at the sam... | YOU ONLY NEED ADVERSARIAL SUPERVISION FOR SEMANTIC IMAGE SYNTHESIS |
d7559933 | Generative adversarial networks (GANs) evolved into one of the most successful unsupervised techniques for generating realistic images. Even though it has recently been shown that GAN training converges, GAN models often end up in local Nash equilibria that are associated with mode collapse or otherwise fail to model t... | Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields |
d202734090 | Learning modular structures which reflect the dynamics of the environment can lead to better generalization and robustness to changes which only affect a few of the underlying causes. We propose Recurrent Independent Mechanisms (RIMs), a new recurrent architecture in which multiple groups of recurrent cells operate wit... | RECURRENT INDEPENDENT MECHANISMS |
d252715808 | k-subset sampling is ubiquitous in machine learning, enabling regularization and interpretability through sparsity. The challenge lies in rendering k-subset sampling amenable to end-to-end learning. This has typically involved relaxing the reparameterized samples to allow for backpropagation, with the risk of introduci... | SIMPLE: A GRADIENT ESTIMATOR FOR k-SUBSET SAMPLING |
d218581596 | This paper presents Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that addresses the fundamental limitations of the popular instance-wise contrastive learning. PCL implicitly encodes semantic structures of the data into the learned embedding space, and prevents the network from... | Prototypical Contrastive Learning of Unsupervised Representations |
d252762386 | Semi-supervised learning and weakly supervised learning are important paradigms that aim to reduce the growing demand for labeled data in current machine learning applications. In this paper, we introduce a novel analysis of the classical label propagation algorithm (LPA) (Zhu & Ghahramani, 2002) that moreover takes ad... | LABEL PROPAGATION WITH WEAK SUPERVISION |
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