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d7167114 | We present a variational approximation to the information bottleneck ofTishby et al. (1999). This variational approach allows us to parameterize the information bottleneck model using a neural network and leverage the reparameterization trick for efficient training. We call this method "Deep Variational Information Bot... | DEEP VARIATIONAL INFORMATION BOTTLENECK |
d252781139 | The brain effortlessly extracts latent causes of stimuli, but how it does this at the network level remains unknown. Most prior attempts at this problem proposed neural networks that implement independent component analysis, which works under the limitation that latent causes are mutually independent. Here, we relax th... | CORRELATIVE INFORMATION MAXIMIZATION BASED BIOLOGICALLY PLAUSIBLE NEURAL NETWORKS FOR CORRELATED SOURCE SEPARATION |
d13757156 | Whilst deep neural networks have shown great empirical success, there is still much work to be done to understand their theoretical properties. In this paper, we study the relationship between random, wide, fully connected, feedforward networks with more than one hidden layer and Gaussian processes with a recursive ker... | Gaussian Process Behaviour in Wide Deep Neural Networks |
d256416154 | Monocular Depth Estimation (MDE) is a critical component in applications suchas autonomous driving. There are various attacks against MDE networks. These attacks, especially the physical ones, pose a great threat to the security of such systems. Traditional adversarial training method requires ground-truth labels hence... | ADVERSARIAL TRAINING OF SELF-SUPERVISED MONOCULAR DEPTH ESTIMATION AGAINST PHYSICAL-WORLD ATTACKS |
d49208503 | Sequential learning, also called lifelong learning, studies the problem of learning tasks in a sequence with access restricted to only the data of the current task. In this paper we look at a scenario with fixed model capacity, and postulate that the learning process should not be selfish, i.e. it should account for fu... | SELFLESS SEQUENTIAL LEARNING |
d202888819 | We design and conduct a simple experiment to study whether neural networks can perform several steps of approximate reasoning in a fixed dimensional latent space. The set of rewrites (i.e. transformations) that can be successfully performed on a statement represents essential semantic features of the statement. We can ... | Mathematical Reasoning in Latent Space |
d257771709 | Most offline reinforcement learning (RL) methods suffer from the trade-off between improving the policy to surpass the behavior policy and constraining the policy to limit the deviation from the behavior policy as computing Q-values using outof-distribution (OOD) actions will suffer from errors due to distributional sh... | OFFLINE RL WITH NO OOD ACTIONS: IN-SAMPLE LEARNING VIA IMPLICIT VALUE REGULARIZATION |
d263829977 | Graphs are a powerful tool for representing and analyzing complex relationships in real-world applications such as social networks, recommender systems, and computational finance.Reasoning on graphs is essential for drawing inferences about the relationships between entities in a complex system, and to identify hidden ... | TALK LIKE A GRAPH: ENCODING GRAPHS FOR LARGE LANGUAGE MODELS |
d220364453 | This paper proposes a new type of implicit generative model that is able to quickly learn a latent representation without an explicit encoder. This is achieved with an implicit neural network that takes as inputs points in the coordinate space alongside a latent vector initialised with zeros. The gradients of the data ... | Gradient Origin Networks |
d184486852 | Discovering causal structure among a set of variables is a fundamental problem in many empirical sciences. Traditional score-based casual discovery methods rely on various local heuristics to search for a directly acyclic graph (DAG) according to a predefined score function. While these methods, e.g., greedy equivalenc... | Causal Discovery with Reinforcement Learning |
d189856873 | Training neural networks with verifiable robustness guarantees is challenging. Several existing approaches utilize linear relaxation based neural network output bounds under perturbation, but they can slow down training by a factor of hundreds depending on the underlying network architectures. Meanwhile, interval bound... | Towards Stable and Efficient Training of Verifiably Robust Neural Networks |
d210849195 | This paper considers multi-agent reinforcement learning (MARL) in networked system control. Specifically, each agent learns a decentralized control policy based on local observations and messages from connected neighbors. We formulate such a networked MARL (NMARL) problem as a spatiotemporal Markov decision process and... | MULTI-AGENT REINFORCEMENT LEARNING FOR NETWORKED SYSTEM CONTROL |
d2906360 | We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning. The approach is to train a neural network to predict properties of the program that generated the outputs from the inputs. We use the neural network's predictions to augment search techni... | DEEPCODER: LEARNING TO WRITE PROGRAMS |
d52898972 | By injecting adversarial examples into training data, adversarial training is promising for improving the robustness of deep learning models. However, most existing adversarial training approaches are based on a specific type of adversarial attack. It may not provide sufficiently representative samples from the adversa... | IMPROVING THE GENERALIZATION OF ADVERSARIAL TRAINING WITH DOMAIN ADAPTATION |
d251320393 | When the available hardware cannot meet the memory and compute requirements to efficiently train high performing machine learning models, a compromise in either the training quality or the model complexity is needed. In Federated Learning (FL), nodes are orders of magnitude more constrained than traditional servergrade... | ZEROFL: EFFICIENT ON-DEVICE TRAINING FOR FEDERATED LEARNING WITH LOCAL SPARSITY |
d258079422 | Language models (LMs) have been instrumental for the rapid advance of natural language processing. This paper studies continual pre-training of LMs, in particular, continual domain-adaptive pre-training (or continual DAP-training). Existing research has shown that further pre-training an LM using a domain corpus to ada... | CONTINUAL PRE-TRAINING OF LANGUAGE MODELS |
d255440621 | Parameter-efficient fine-tuning aims to achieve performance comparable to fine-tuning, using fewer trainable parameters. Several strategies (e.g., Adapters, prefix tuning, BitFit, and LoRA) have been proposed. However, their designs are hand-crafted separately, and it remains unclear whether certain design patterns exi... | PARAMETER-EFFICIENT FINE-TUNING DESIGN SPACES |
d229297679 | At the heart of many robotics problems is the challenge of learning correspondences across domains. For instance, imitation learning requires obtaining correspondence between humans and robots; sim-to-real requires correspondence between physics simulators and the real world; transfer learning requires correspondences ... | LEARNING CROSS-DOMAIN CORRESPONDENCE FOR CONTROL WITH DYNAMICS CYCLE-CONSISTENCY |
d244527108 | We propose learning via retracing, a novel self-supervised approach for learning the state representation (and the associated dynamics model) for reinforcement learning tasks. In addition to the predictive (reconstruction) supervision in the forward direction, we propose to include "retraced" transitions for representa... | LEARNING STATE REPRESENTATIONS VIA RETRACING IN REINFORCEMENT LEARNING |
d231985599 | Recently, various auxiliary tasks have been proposed to accelerate representation learning and improve sample efficiency in deep reinforcement learning (RL). However, existing auxiliary tasks do not take the characteristics of RL problems into consideration and are unsupervised. By leveraging returns, the most importan... | RETURN-BASED CONTRASTIVE REPRESENTATION LEARNING FOR REINFORCEMENT LEARNING |
d254926918 | Biological vision systems are unparalleled in their ability to learn visual representations without supervision. In machine learning, self-supervised learning (SSL) has led to major advances in forming object representations in an unsupervised fashion. Such systems learn representations invariant to augmentation operat... | TIME TO AUGMENT SELF-SUPERVISED VISUAL REPRESENTATION LEARNING |
d229923865 | A generalist robot must be able to complete a variety of tasks in its environment. One appealing way to specify each task is in terms of a goal observation. However, learning goal-reaching policies with reinforcement learning remains a challenging problem, particularly when hand-engineered reward functions are not avai... | MODEL-BASED VISUAL PLANNING WITH SELF-SUPERVISED FUNCTIONAL DISTANCES |
d260350986 | The recent progress in large language models (LLMs), especially the invention of chain-of-thought prompting, has made it possible to automatically answer questions by stepwise reasoning. However, when faced with more complicated problems that require non-linear thinking, even the strongest LLMs make mistakes. To addres... | SELFCHECK: USING LLMS TO ZERO-SHOT CHECK THEIR OWN STEP-BY-STEP REASONING |
d263605855 | Making LLaMA SEE and Draw with SEED Tokenizer | |
d202750126 | In natural language processing, it has been observed recently that generalization could be greatly improved by finetuning a large-scale language model pretrained on a large unlabeled corpus. Despite its recent success and wide adoption, finetuning a large pretrained language model on a downstream task is prone to degen... | MIXOUT: EFFECTIVE REGULARIZATION TO FINETUNE LARGE-SCALE PRETRAINED LANGUAGE MODELS |
d3525078 | We give a simple, fast algorithm for hyperparameter optimization inspired by techniques from the analysis of Boolean functions. We focus on the high-dimensional regime where the canonical example is training a neural network with a large number of hyperparameters. The algorithm-an iterative application of compressed se... | Hyperparameter Optimization: A Spectral Approach |
d248084856 | Characterizing the separation power of graph neural networks (GNNs) provides an understanding of their limitations for graph learning tasks. Results regarding separation power are, however, usually geared at specific GNN architectures, and tools for understanding arbitrary GNN architectures are generally lacking. We pr... | EXPRESSIVENESS AND APPROXIMATION PROPERTIES OF GRAPH NEURAL NETWORKS |
d254198988 | Mixup, which creates synthetic training instances by linearly interpolating random sample pairs, is a simple and yet effective regularization technique to boost the performance of deep models trained with SGD. In this work, we report a previously unobserved phenomenon in Mixup training: on a number of standard datasets... | OVER-TRAINING WITH MIXUP MAY HURT GENERALIZATION |
d44981793 | Recent breakthroughs in computer vision make use of large deep neural networks, utilizing the substantial speedup offered by GPUs. For applications running on limited hardware however, high precision real-time processing can still be a challenge. One approach to solve this problem is learning networks with binary or te... | Learning Discrete Weights Using the Local Reparameterization Trick |
d253581364 | Various applications of voice synthesis have been developed independently despite the fact that they generate "voice" as output in common. In addition, most of the voice synthesis models still require a large number of audio data paired with annotated labels (e.g., text transcription and music score) for training. To t... | NANSY++: UNIFIED VOICE SYNTHESIS WITH NEURAL ANALYSIS AND SYNTHESIS |
d251018503 | Test-time adaptation (TTA) aims to adapt a trained classifier using online unlabeled test data only, without any information related to the training procedure. Most existing TTA methods adapt the trained classifier using the classifier's prediction on the test data as pseudo-label. However, under test-time domain shift... | TEST-TIME ADAPTATION VIA SELF-TRAINING WITH NEAREST NEIGHBOR INFORMATION |
d250626929 | Recently the SP (Stochastic Polyak step size) method has emerged as a competitive adaptive method for setting the step sizes of SGD. SP can be interpreted as a method specialized to interpolated models, since it solves the interpolation equations. SP solves these equation by using local linearizations of the model. We ... | SP2 : A Second Order Stochastic Polyak Method |
d252683534 | Evaluating hypothetical statements about how the world would be had a different course of action been taken is arguably one key capability expected from modern AI systems. Counterfactual reasoning underpins discussions in fairness, the determination of blame and responsibility, credit assignment, and regret. In this pa... | NEURAL CAUSAL MODELS FOR COUNTERFACTUAL IDENTIFICATION AND ESTIMATION |
d231847056 | Graph Representation Learning (GRL) methods have impacted fields from chemistry to social science. However, their algorithmic implementations are specialized to specific use-cases e.g. message passing methods are run differently from node embedding ones. Despite their apparent differences, all these methods utilize the... | GRAPH TRAVERSAL WITH TENSOR FUNCTIONALS: A META-ALGORITHM FOR SCALABLE LEARNING |
d260378993 | Sparse mixture of expert architectures (MoEs) scale model capacity without large increases in training or inference costs. Despite their success, MoEs suffer from a number of issues: training instability, token dropping, inability to scale the number of experts, or ineffective finetuning. In this work, we propose Soft ... | From Sparse to Soft Mixtures of Experts |
d235743242 | In observational studies, balancing covariates in different treatment groups is essential to estimate treatment effects. One of the most commonly used methods for such purposes is weighting. The performance of this class of methods usually depends on strong regularity conditions for the underlying model, which might no... | Treatment Effects Estimation by Uniform Transformer |
d52901536 | While deep neural networks are a highly successful model class, their large memory footprint puts considerable strain on energy consumption, communication bandwidth, and storage requirements. Consequently, model size reduction has become an utmost goal in deep learning. A typical approach is to train a set of determini... | MINIMAL RANDOM CODE LEARNING: GETTING BITS BACK FROM COMPRESSED MODEL PARAMETERS |
d247450787 | We study the problem of learning worst-case-safe parameters for programs that use neural networks as well as symbolic, human-written code. Such neurosymbolic programs arise in many safety-critical domains. However, because they can use nondifferentiable operations, it is hard to learn their parameters using existing gr... | SAFE NEUROSYMBOLIC LEARNING WITH DIFFERENTIABLE SYMBOLIC EXECUTION |
d252693138 | Traditional recurrent neural networks (RNNs) have a fixed, finite number of memory cells. In theory (assuming bounded range and precision), this limits their formal language recognition power to regular languages, and in practice, RNNs have been shown to be unable to learn many context-free languages (CFLs). In order t... | THE SURPRISING COMPUTATIONAL POWER OF NONDETERMINISTIC STACK RNNS |
d229153786 | In the human brain, sequences of language input are processed within a distributed and hierarchical architecture, in which higher stages of processing encode contextual information over longer timescales. In contrast, in recurrent neural networks which perform natural language processing, we know little about how the m... | MAPPING THE TIMESCALE ORGANIZATION OF NEURAL LANGUAGE MODELS |
d248118520 | Neural processes (NPs) aim to stochastically complete unseen data points based on a given context dataset. NPs essentially leverage a given dataset as a context representation to derive a suitable identifier for a novel task. To improve the prediction accuracy, many variants of NPs have investigated context embedding a... | NEURAL PROCESSES WITH STOCHASTIC ATTENTION: PAYING MORE ATTENTION TO THE CONTEXT DATASET |
d247696299 | Recurrent State-space models (RSSMs) are highly expressive models for learning patterns in time series data and system identification.However, these models assume that the dynamics are fixed and unchanging, which is rarely the case in realworld scenarios.Many control applications often exhibit tasks with similar but no... | HIDDEN PARAMETER RECURRENT STATE SPACE MODELS FOR CHANGING DYNAMICS SCENARIOS |
d225094654 | Despite the widespread application of recurrent neural networks (RNNs), a unified understanding of how RNNs solve particular tasks remains elusive. In particular, it is unclear what dynamical patterns arise in trained RNNs, and how those patterns depend on the training dataset or task. This work addresses these questio... | THE GEOMETRY OF INTEGRATION IN TEXT CLASSIFICATION RNNS |
d36549760 | Deep generative neural networks have proven effective at both conditional and unconditional modeling of complex data distributions. Conditional generation enables interactive control, but creating new controls often requires expensive retraining. In this paper, we develop a method to condition generation without retrai... | LATENT CONSTRAINTS: LEARNING TO GENERATE CONDITIONALLY FROM UNCONDITIONAL GENERATIVE MODELS |
d263830051 | Optimal Transport is a useful metric to compare probability distributions and to compute a pairing given a ground cost.Its entropic regularization variant (eOT) is crucial to have fast algorithms and reflect fuzzy/noisy matchings.This work focuses on Inverse Optimal Transport (iOT), the problem of inferring the ground ... | Sparsistency for Inverse Optimal Transport |
d239015813 | Graph Neural Networks (GNNs) are popular for graph machine learning and have shown great results on wide node classification tasks. Yet, they are less popular for practical deployments in the industry owing to their scalability challenges incurred by data dependency. Namely, GNN inference depends on neighbor nodes mult... | GRAPH-LESS NEURAL NETWORKS: TEACHING OLD MLPS NEW TRICKS VIA DISTILLATION |
d259088843 | Agents navigating in 3D environments require some form of memory, which should hold a compact and actionable representation of the history of observations useful for decision taking and planning. In most end-to-end learning approaches the representation is latent and usually does not have a clearly defined interpretati... | LEARNING WITH A MOLE: TRANSFERABLE LATENT SPATIAL REPRESENTATIONS FOR NAVIGATION WITHOUT RECONSTRUCTION |
d263609006 | An essential and challenging problem in causal inference is causal effect estimation from observational data.The problem becomes more difficult with the presence of unobserved confounding variables.The front-door adjustment is a practical approach for dealing with unobserved confounding variables.However, the restricti... | CAUSAL INFERENCE WITH CONDITIONAL FRONT-DOOR ADJUSTMENT AND IDENTIFIABLE VARIATIONAL AUTOENCODER |
d263830401 | We introduce ODEFormer, the first transformer able to infer multidimensional ordinary differential equation (ODE) systems in symbolic form from the observation of a single solution trajectory.We perform extensive evaluations on two datasets: (i) the existing 'Strogatz' dataset featuring twodimensional systems; (ii) ODE... | ODEFormer: Symbolic Regression of Dynamical Systems with Transformers |
d227746856 | Consider a prediction setting where a few inputs (e.g., satellite images) are expensively annotated with the prediction targets (e.g., crop types), and many inputs are cheaply annotated with auxiliary information (e.g., climate information). How should we best leverage this auxiliary information for the prediction task... | In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness |
d56895592 | Generative adversarial nets (GANs) are widely used to learn the data sampling process and their performance may heavily depend on the loss functions, given a limited computational budget. This study revisits MMD-GAN that uses the maximum mean discrepancy (MMD) as the loss function for GAN and makes two contributions. F... | IMPROVING MMD-GAN TRAINING WITH REPULSIVE LOSS FUNCTION |
d67856640 | Recent advances in conditional image generation tasks, such as image-to-image translation and image inpainting, are largely accounted to the success of conditional GAN models, which are often optimized by the joint use of the GAN loss with the reconstruction loss However, we reveal that this training recipe shared by a... | HARMONIZING MAXIMUM LIKELIHOOD WITH GANS FOR MULTIMODAL CONDITIONAL GENERATION |
d252545361 | Deep neural networks trained end-to-end to map a measurement of a (noisy) image to a clean image perform excellent for a variety of linear inverse problems. Current methods are only trained on a few hundreds or thousands of images as opposed to the millions of examples deep networks are trained on in other domains. In ... | Scaling Laws For Deep Learning Based Image Reconstruction |
d221095529 | We develop a methodology for assessing the robustness of models to subpopulation shift-specifically, their ability to generalize to novel data subpopulations that were not observed during training. Our approach leverages the class structure underlying existing datasets to control the data subpopulations that comprise t... | BREEDS: Benchmarks for Subpopulation Shift |
d52902256 | As shown in recent research, deep neural networks can perfectly fit randomly labeled data, but with very poor accuracy on held out data. This phenomenon indicates that loss functions such as cross-entropy are not a reliable indicator of generalization. This leads to the crucial question of how generalization gap should... | Predicting the Generalization Gap in Deep Networks with Margin Distributions |
d3524955 | Interpreting deep neural networks can enable new applications for predictive modeling where both accuracy and interpretability are required. In this paper, we examine the weights of a deep neural network to interpret the statistical interactions it captures. Our key observation is that any input features that interact ... | Detecting Statistical Interactions from Neural Network Weights |
d246634950 | Random pruning is arguably the most naive way to attain sparsity in neural networks, but has been deemed uncompetitive by either post-training pruning or sparse training. In this paper, we focus on sparse training and highlight a perhaps counter-intuitive finding, that random pruning at initialization can be quite powe... | THE UNREASONABLE EFFECTIVENESS OF RANDOM PRUNING: RETURN OF THE MOST NAIVE BASELINE FOR SPARSE TRAINING |
d246680129 | Black-box optimization formulations for biological sequence design have drawn recent attention due to their promising potential impact on the pharmaceutical industry. In this work, we propose to unify two seemingly distinct worlds: likelihood-free inference and black-box optimization, under one probabilistic framework.... | UNIFYING LIKELIHOOD-FREE INFERENCE WITH BLACK-BOX OPTIMIZATION AND BEYOND |
d263834884 | As research in large language models (LLMs) continues to accelerate, LLM-based evaluation has emerged as a scalable and cost-effective alternative to human evaluations for comparing the ever increasing list of models. This paper investigates the efficacy of these "LLM evaluators", particularly in using them to assess i... | EVALUATING LARGE LANGUAGE MODELS AT EVALUATING INSTRUCTION FOLLOWING |
d238583580 | Fine-tuning large pretrained language models on downstream tasks has become the de-facto learning paradigm in NLP.However, conventional approaches finetune all the parameters of the pretrained model, which becomes prohibitive as the model size and the number of tasks grow.Recent work has proposed a variety of parameter... | TOWARDS A UNIFIED VIEW OF PARAMETER-EFFICIENT TRANSFER LEARNING |
d235613427 | We propose Multi-Level Local SGD, a distributed stochastic gradient method for learning a smooth, non-convex objective in a multi-level communication network with heterogeneous workers. Our network model consists of a set of disjoint subnetworks, with a single hub and multiple workers; further, workers may have differe... | MULTI-LEVEL LOCAL SGD: DISTRIBUTED SGD FOR HETEROGENEOUS HIERARCHICAL NETWORKS |
d252780443 | Existing pre-trained models are generally geared towards a particular class of problems. To date, there seems to be still no consensus on what the right architecture and pre-training setup should be. This paper presents a unified framework for pre-training models that are universally effective across datasets and setup... | UL2: Unifying Language Learning Paradigms |
d265038030 | 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 |
d253238010 | Point cloud data is ubiquitous in scientific fields. Recently, geometric deep learning (GDL) has been widely applied to solve prediction tasks with such data. However, GDL models are often complicated and hardly interpretable, which poses concerns to scientists who are to deploy these models in scientific analysis and ... | INTERPRETABLE GEOMETRIC DEEP LEARNING VIA LEARNABLE RANDOMNESS INJECTION |
d256503575 | Visual imitation learning enables reinforcement learning agents to learn to behave from expert visual demonstrations such as videos or image sequences, without explicit, well-defined rewards. Previous research either adopted supervised learning techniques or induce simple and coarse scalar rewards from pixels, neglecti... | VISUAL IMITATION LEARNING WITH PATCH REWARDS |
d208909759 | The ability to learn new concepts with small amounts of data is a critical aspect of intelligence that has proven challenging for deep learning methods. Meta-learning has emerged as a promising technique for leveraging data from previous tasks to enable efficient learning of new tasks. However, most meta-learning algor... | META-LEARNING WITHOUT MEMORIZATION |
d255340832 | The ability to jointly learn from multiple modalities, such as text, audio, and visual data, is a defining feature of intelligent systems. While there have been promising advances in designing neural networks to harness multimodal data, the enormous success of data augmentation currently remains limited to single-modal... | LEARNING MULTIMODAL DATA AUGMENTATION IN FEATURE SPACE |
d211068995 | We consider the large-scale query-document retrieval problem: given a query (e.g., a question), return the set of relevant documents (e.g., paragraphs containing the answer) from a large document corpus. This problem is often solved in two steps. The retrieval phase first reduces the solution space, returning a subset ... | PRE-TRAINING TASKS FOR EMBEDDING-BASED LARGE-SCALE RETRIEVAL |
d219530758 | Learning disentangled representations is regarded as a fundamental task for improving the generalization, robustness, and interpretability of generative models. However, measuring disentanglement has been challenging and inconsistent, often dependent on an ad-hoc external model or specific to a certain dataset. To addr... | Evaluating the Disentanglement of Deep Generative Models through Manifold Topology |
d86393936 | We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics. As such, the... | SPECTRAL INFERENCE NETWORKS: UNIFYING DEEP AND SPECTRAL LEARNING |
d252872842 | For generative autoencoders to learn a meaningful latent representation for data generation, a careful balance must be achieved between reconstruction error and how close the distribution in the latent space is to the prior. However, this balance is challenging to achieve due to a lack of criteria that work both at the... | AUTO-ENCODING GOODNESS OF FIT |
d247222625 | A topic model is often formulated as a generative model that explains how each word of a document is generated given a set of topics and document-specific topic proportions. It is focused on capturing the word co-occurrences in a document and hence often suffers from poor performance in analyzing short documents. In ad... | REPRESENTING MIXTURES OF WORD EMBEDDINGS WITH MIXTURES OF TOPIC EMBEDDINGS |
d54434799 | Recurrent neural networks (RNNs) are an effective representation of control policies for a wide range of reinforcement and imitation learning problems. RNN policies, however, are particularly difficult to explain, understand, and analyze due to their use of continuous-valued memory vectors and observation features.In t... | LEARNING FINITE STATE REPRESENTATIONS OF RECURRENT POLICY NETWORKS |
d226281876 | Model-based planning is often thought to be necessary for deep, careful reasoning and generalization in artificial agents. While recent successes of model-based reinforcement learning (MBRL) with deep function approximation have strengthened this hypothesis, the resulting diversity of model-based methods has also made ... | ON THE ROLE OF PLANNING IN MODEL-BASED DEEP REINFORCEMENT LEARNING |
d220920191 | Recent studies have demonstrated the overwhelming advantage of cross-lingual pre-trained models (PTMs), such as multilingual BERT and XLM, on crosslingual NLP tasks. However, existing approaches essentially capture the cooccurrence among tokens through involving the masked language model (MLM) objective with token-leve... | ON LEARNING UNIVERSAL REPRESENTATIONS ACROSS LANGUAGES |
d246634506 | There is increasing evidence suggesting neural networks' sensitivity to distribution shifts, so that research on out-of-distribution (OOD) generalization comes into the spotlight. Nonetheless, current endeavors mostly focus on Euclidean data, and its formulation for graph-structured data is not clear and remains under-... | HANDLING DISTRIBUTION SHIFTS ON GRAPHS: AN INVARIANCE PERSPECTIVE |
d263671952 | Generative Flow Networks (GFlowNets) are amortized samplers that learn stochastic policies to sequentially generate compositional objects from a given unnormalized reward distribution.They can generate diverse sets of high-reward objects, which is an important consideration in scientific discovery tasks.However, as the... | Pre-Training and Fine-Tuning Generative Flow Networks |
d263830894 | We show how to obtain improved active learning methods in the agnostic (adversarial noise) setting by combining marginal leverage score sampling with nonindependent sampling strategies that promote spatial coverage.In particular, we propose an easily implemented method based on the pivotal sampling algorithm, which we ... | IMPROVED ACTIVE LEARNING VIA DEPENDENT LEVERAGE SCORE SAMPLING |
d233287919 | The predominant approach for language modeling is to process sequences from left to right, but this eliminates a source of information: the order by which the sequence was generated. One strategy to recover this information is to decode both the content and ordering of tokens. Existing approaches supervise content and ... | DISCOVERING NON-MONOTONIC AUTOREGRESSIVE ORDERINGS WITH VARIATIONAL INFERENCE |
d257232422 | Parametrizations of data manifolds in shape spaces can be computed using the rich toolbox of Riemannian geometry. This, however, often comes with high computational costs, which raises the question if one can learn an efficient neural network approximation. We show that this is indeed possible for shape spaces with a s... | PARAMETRIZING PRODUCT SHAPE MANIFOLDS BY COMPOSITE NETWORKS |
d252668917 | Program synthesis strives to generate a computer program as a solution to a given problem specification, expressed with input-output examples or natural language descriptions. The prevalence of large language models advances the state-of-the-art for program synthesis, though limited training resources and data impede o... | CODEGEN: AN OPEN LARGE LANGUAGE MODEL FOR CODE WITH MULTI-TURN PROGRAM SYNTHESIS |
d232170613 | Many potential applications of reinforcement learning (RL) require guarantees that the agent will perform well in the face of disturbances to the dynamics or reward function. In this paper, we prove theoretically that maximum entropy (MaxEnt) RL maximizes a lower bound on a robust RL objective, and thus can be used to ... | MAXIMUM ENTROPY RL (PROVABLY) SOLVES SOME ROBUST RL PROBLEMS |
d264426077 | Proximal operators are ubiquitous in inverse problems, commonly appearing as part of algorithmic strategies to regularize problems that are otherwise ill-posed.Modern deep learning models have been brought to bear for these tasks too, as in the framework of plug-and-play or deep unrolling, where they loosely resemble p... | What's in a Prior? Learned Proximal Networks for Inverse Problems |
d52911374 | Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards. However, this places on environment designers the onus of designing language-conditional reward functions which may not be easily or tractably implemented as the complexity o... | LEARNING TO UNDERSTAND GOAL SPECIFICATIONS BY MODELLING REWARD |
d238419650 | With the discovery of Wasserstein GANs, Optimal Transport (OT) has become a powerful tool for large-scale generative modeling tasks. In these tasks, OT cost is typically used as the loss for training GANs. In contrast to this approach, we show that the OT map itself can be used as a generative model, providing comparab... | GENERATIVE MODELING WITH OPTIMAL TRANSPORT MAPS |
d9128667 | Predicting the future in real-world settings, particularly from raw sensory observations such as images, is exceptionally challenging. Real-world events can be stochastic and unpredictable, and the high dimensionality and complexity of natural images require the predictive model to build an intricate understanding of t... | STOCHASTIC VARIATIONAL VIDEO PREDICTION |
d257219444 | The standard paradigm of neural language generation adopts maximum likelihood estimation (MLE) as the optimizing method.From a distributional view, MLE in fact minimizes the Kullback-Leibler divergence (KLD) between the distribution of the real data and that of the model.However, this approach forces the model to distr... | TAILORING LANGUAGE GENERATION MODELS UNDER TOTAL VARIATION DISTANCE |
d223953610 | Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision. Most representation learning approaches rely solely on visual data such as images or videos. In this paper, we explore a novel approach, where we use human interaction and attent... | What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions |
d3508167 | Depthwise separable convolutions reduce the number of parameters and computation used in convolutional operations while increasing representational efficiency. They have been shown to be successful in image classification models, both in obtaining better models than previously possible for a given parameter count (the ... | Depthwise Separable Convolutions for Neural Machine Translation |
d108306873 | We present the perceptor gradients algorithm -a novel approach to learning symbolic representations based on the idea of decomposing an agent's policy into i) a perceptor network extracting symbols from raw observation data and ii) a task encoding program which maps the input symbols to output actions. We show that the... | LEARNING PROGRAMMATICALLY STRUCTURED REPRESENTATIONS WITH PERCEPTOR GRADIENTS |
d242784472 | Reinforcement learning encounters many challenges when applied directly in the real world. Sim-to-real transfer is widely used to transfer the knowledge learned from simulation to the real world. Domain randomization-one of the most popular algorithms for sim-to-real transfer-has been demonstrated to be effective in va... | UNDERSTANDING DOMAIN RANDOMIZATION FOR SIM-TO-REAL TRANSFER |
d253117027 | Steganography is the practice of encoding secret information into innocuous content in such a manner that an adversarial third party would not realize that there is hidden meaning.While this problem has classically been studied in security literature, recent advances in generative models have led to a shared interest a... | PERFECTLY SECURE STEGANOGRAPHY USING MINIMUM ENTROPY COUPLING |
d259286823 | Hypernetworks, neural networks that predict the parameters of another neural network, are powerful models that have been successfully used in diverse applications from image generation to multi-task learning. Unfortunately, existing hypernetworks are often challenging to train. Training typically converges far more slo... | Magnitude Invariant Parametrizations Improve Hypernetwork Learning |
d252544798 | Error correction code (ECC) is an integral part of the physical communication layer, ensuring reliable data transfer over noisy channels. Recently, neural decoders have demonstrated their advantage over classical decoding techniques. However, recent state-of-the-art neural decoders suffer from high complexity and lack ... | DENOISING DIFFUSION ERROR CORRECTION CODES |
d246411225 | We present in this paper a novel query formulation using dynamic anchor boxes for DETR (DEtection TRansformer) and offer a deeper understanding of the role of queries in DETR. This new formulation directly uses box coordinates as queries in Transformer decoders and dynamically updates them layer-by-layer. Using box coo... | DAB-DETR: DYNAMIC ANCHOR BOXES ARE BETTER QUERIES FOR DETR |
d259108693 | Learning features from data is one of the defining characteristics of deep learning, but our theoretical understanding of the role features play in deep learning is still rudimentary. To address this gap, we introduce a new tool, the interaction tensor, for empirically analyzing the interaction between data and model t... | On the Joint Interaction of Models, Data, and Features |
d213152193 | Masked language modeling (MLM) pre-training methods such as BERT corrupt the input by replacing some tokens with [MASK] and then train a model to reconstruct the original tokens. While they produce good results when transferred to downstream NLP tasks, they generally require large amounts of compute to be effective. A... | ELECTRA: PRE-TRAINING TEXT ENCODERS AS DISCRIMINATORS RATHER THAN GENERATORS |
d247244684 | Given two point sets, the problem of registration is to recover a transformation that matches one set to the other. This task is challenging due to the presence of the large number of outliers, the unknown non-rigid deformations and the large sizes of point sets. To obtain strong robustness against outliers, we formula... | PARTIAL WASSERSTEIN ADVERSARIAL NETWORK FOR NON-RIGID POINT SET REGISTRATION |
d253018433 | A natural goal in multiagent learning besides finding equilibria is to learn rationalizable behavior, where players learn to avoid iteratively dominated actions. However, even in the basic setting of multiplayer general-sum games, existing algorithms require a number of samples exponential in the number of players to l... | Learning Rationalizable Equilibria in Multiplayer Games |
d261493986 | Rotary Position Embeddings (RoPE) have been shown to effectively encode positional information in transformer-based language models.However, these models fail to generalize past the sequence length they were trained on.We present YaRN (Yet another RoPE extensioN method), a compute-efficient method to extend the context... | YaRN: Efficient Context Window Extension of Large Language Models |
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