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d261530381 | Federated Learning (FL) has gained significant attraction due to its ability to enable privacy-preserving training over decentralized data.Current literature in FL mostly focuses on single-task learning.However, over time, new tasks may appear in the clients and the global model should learn these tasks without forgett... | FEDERATED ORTHOGONAL TRAINING: MITIGATING GLOBAL CATASTROPHIC FORGETTING IN CONTINUAL FEDERATED LEARNING |
d235606307 | Multimodal variational autoencoders (VAEs) seek to model the joint distribution over heterogeneous data (e.g. vision, language), whilst also capturing a shared representation across such modalities. Prior work has typically combined information from the modalities by reconciling idiosyncratic representations directly i... | LEARNING MULTIMODAL VAES THROUGH MUTUAL SUPERVISION |
d195886377 | The ability to forecast a set of likely yet diverse possible future behaviors of an agent (e.g., future trajectories of a pedestrian) is essential for safety-critical perception systems (e.g., autonomous vehicles). In particular, a set of possible future behaviors generated by the system must be diverse to account for ... | DIVERSE TRAJECTORY FORECASTING WITH DETERMINANTAL POINT PROCESSES |
d214002473 | We propose a model that is able to perform unsupervised physical parameter estimation of systems from video, where the differential equations governing the scene dynamics are known, but labeled states or objects are not available. Existing physical scene understanding methods require either object state supervision, or... | PHYSICS-AS-INVERSE-GRAPHICS: UNSUPERVISED PHYSICAL PARAMETER ESTIMATION FROM VIDEO |
d254017742 | Particle-based variational inference (VI) minimizes the KL divergence between model samples and the target posterior with gradient flow estimates. With the popularity of Stein variational gradient descent (SVGD), the focus of particlebased VI algorithms has been on the properties of functions in Reproducing Kernel Hilb... | PARTICLE-BASED VARIATIONAL INFERENCE WITH PRECONDITIONED FUNCTIONAL GRADIENT FLOW |
d220793552 | While successful for various computer vision tasks, deep neural networks have shown to be vulnerable to texture style shifts and small perturbations to which humans are robust. Hence, our goal is to train models in such a way that improves their robustness to these perturbations. We are motivated by the approximately s... | Robust and Generalizable Visual Representation Learning via Random Convolutions |
d256105351 | Complex Query Answering (CQA) over Knowledge Graphs (KGs) has attracted a lot of attention to potentially support many applications. Given that KGs are usually incomplete, neural models are proposed to answer the logical queries by parameterizing set operators with complex neural networks. However, such methods usually... | LOGICAL MESSAGE PASSING NETWORKS WITH ONE-HOP INFERENCE ON ATOMIC FORMULAS |
d4630420 | Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks. We propose Graph2Gauss -an approach that can efficiently learn versatile node embeddings on large scale (attributed) graphs that show strong performance on tasks such as link... | DEEP GAUSSIAN EMBEDDING OF GRAPHS: UNSUPERVISED INDUCTIVE LEARNING VIA RANKING |
d259075184 | A promising direction for pre-training 3D point clouds is to leverage the massive amount of data in 2D, whereas the domain gap between 2D and 3D creates a fundamental challenge.This paper proposes a novel approach to point-cloud pre-training that learns 3D representations by leveraging pre-trained 2D networks.Different... | MULTI-VIEW REPRESENTATION IS WHAT YOU NEED FOR POINT-CLOUD PRE-TRAINING |
d263153393 | We propose to replace vector quantization (VQ) in the latent representation of VQ-VAEs with a simple scheme termed finite scalar quantization (FSQ), where we project the VAE representation down to a few dimensions (typically less than 10).Each dimension is quantized to a small set of fixed values, leading to an (implic... | FINITE SCALAR QUANTIZATION: VQ-VAE MADE SIMPLE |
d258048805 | Generative modelling over continuous-time geometric constructs, a.k.a chirographic data such as handwriting, sketches, drawings etc., have been accomplished through autoregressive distributions. Such strictly-ordered discrete factorization however falls short of capturing key properties of chirographic data -it fails t... | CHIRODIFF: MODELLING CHIROGRAPHIC DATA WITH DIFFUSION MODELS |
d221971169 | Although neural language models are effective at capturing statistics of natural language, their representations are challenging to interpret. In particular, it is unclear how these models retain information over multiple timescales. In this work, we construct explicitly multi-timescale language models by manipulating ... | Multi-timescale representation learning in LSTM Language Models |
d259203153 | Federated learning (FL) is a distributed machine learning framework where the global model of a central server is trained via multiple collaborative steps by participating clients without sharing their data.While being a flexible framework, where the distribution of local data, participation rate, and computing power o... | ADAPTIVE FEDERATED LEARNING WITH AUTO-TUNED CLIENTS |
d252715881 | The idea of embedding optimization problems into deep neural networks as optimization layers to encode constraints and inductive priors has taken hold in recent years. Most existing methods focus on implicitly differentiating Karush-Kuhn-Tucker (KKT) conditions in a way that requires expensive computations on the Jacob... | Alternating Differentiation for Optimization Layers |
d52894384 | Humans learn to solve tasks of increasing complexity by building on top of previously acquired knowledge. Typically, there exists a natural progression in the tasks that we learn -most do not require completely independent solutions, but can be broken down into simpler subtasks. We propose to represent a solver for eac... | VISUAL REASONING BY PROGRESSIVE MODULE NETWORKS |
d249017891 | Animals thrive in a constantly changing environment and leverage the temporal structure to learn well-factorized causal representations. In contrast, traditional neural networks suffer from forgetting in changing environments and many methods have been proposed to limit forgetting with different trade-offs. Inspired by... | THALAMUS: A BRAIN-INSPIRED ALGORITHM FOR BIOLOGICALLY-PLAUSIBLE CONTINUAL LEARNING AND DISENTANGLED REPRESENTATIONS |
d57759319 | Learning a policy using only observational data is challenging because the distribution of states it induces at execution time may differ from the distribution observed during training. We propose to train a policy by unrolling a learned model of the environment dynamics over multiple time steps while explicitly penali... | MODEL-PREDICTIVE POLICY LEARNING WITH UNCERTAINTY REGULARIZATION FOR DRIVING IN DENSE TRAFFIC |
d257631995 | Recently, graph-based planning algorithms have gained much attention to solve goal-conditioned reinforcement learning (RL) tasks: they provide a sequence of subgoals to reach the target-goal, and the agents learn to execute subgoalconditioned policies. However, the sample-efficiency of such RL schemes still remains a c... | IMITATING GRAPH-BASED PLANNING WITH GOAL-CONDITIONED POLICIES |
d231839495 | While deep neural networks show great performance on fitting to the training distribution, improving the networks' generalization performance to the test distribution and robustness to the sensitivity to input perturbations still remain as a challenge. Although a number of mixup based augmentation strategies have been ... | Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity |
d257353458 | Real world applications of Reinforcement Learning (RL) are often partially observable, thus requiring memory. Despite this, partial observability is still largely ignored by contemporary RL benchmarks and libraries. We introduce Partially Observable Process Gym (POPGym), a two-part library containing (1) a diverse coll... | POPGYM: BENCHMARKING PARTIALLY OBSERVABLE REINFORCEMENT LEARNING |
d67856680 | Answerer in Questioner's Mind (AQM) is an information-theoretic framework that has been recently proposed for task-oriented dialog systems. AQM benefits from asking a question that would maximize the information gain when it is asked. However, due to its intrinsic nature of explicitly calculating the information gain, ... | LARGE-SCALE ANSWERER IN QUESTIONER'S MIND FOR VISUAL DIALOG QUESTION GENERATION |
d231740484 | Deep learning models require a large amount of data to perform well. When data is scarce for a target task, we can transfer the knowledge gained by training on similar tasks to quickly learn the target. A successful approach is meta-learning, or learning to learn a distribution of tasks, where learning is represented b... | META-LEARNING WITH NEGATIVE LEARNING RATES |
d203836948 | We propose an inductive matrix completion model without using side information. By factorizing the (rating) matrix into the product of low-dimensional latent embeddings of rows (users) and columns (items), a majority of existing matrix completion methods are transductive, since the learned embeddings cannot generalize ... | INDUCTIVE MATRIX COMPLETION BASED ON GRAPH NEURAL NETWORKS |
d222380524 | We propose a general framework for searching surrogate losses for mainstream semantic segmentation metrics. This is in contrast to existing loss functions manually designed for individual metrics. The searched surrogate losses can generalize well to other datasets and networks. Extensive experiments on PASCAL VOC and C... | AUTO SEG-LOSS: SEARCHING METRIC SURROGATES FOR SEMANTIC SEGMENTATION |
d238744427 | In this paper, we propose THOMAS, a joint multi-agent trajectory prediction framework allowing for an efficient and consistent prediction of multi-agent multimodal trajectories. We present a unified model architecture for simultaneous agent future heatmap estimation, in which we leverage hierarchical and sparse image g... | THOMAS: TRAJECTORY HEATMAP OUTPUT WITH LEARNED MULTI-AGENT SAMPLING |
d238419701 | We present a novel framework, InfinityGAN, for arbitrary-sized image generation. The task is associated with several key challenges. First, scaling existing models to an arbitrarily large image size is resource-constrained, in terms of both computation and availability of large-field-of-view training data. InfinityGAN ... | INFINITYGAN: TOWARDS INFINITE-PIXEL IMAGE SYNTHESIS |
d209202457 | When performing imitation learning from expert demonstrations, distribution matching is a popular approach, in which one alternates between estimating distribution ratios and then using these ratios as rewards in a standard reinforcement learning (RL) algorithm. Traditionally, estimation of the distribution ratio requi... | IMITATION LEARNING VIA OFF-POLICY DISTRIBUTION MATCHING |
d195791369 | Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment. A good model can potentially enable planning algorithms to generate a large variety of behaviors and solve diverse tasks. However, learning an accurate model for complex dynamical systems is diffi... | DYNAMICS-AWARE UNSUPERVISED DISCOVERY OF SKILLS |
d258999368 | Despite the impressive generalization capabilities of deep neural networks, they have been repeatedly shown to be overconfident when they are wrong. Fixing this issue is known as model calibration, and has consequently received much attention in the form of modified training schemes and post-training calibration proced... | ON THE LIMITATIONS OF TEMPERATURE SCALING FOR DISTRIBUTIONS WITH OVERLAPS |
d248239812 | Model-based reinforcement learning (RL) algorithms designed for handling complex visual observations typically learn some sort of latent state representation, either explicitly or implicitly. Standard methods of this sort do not distinguish between functionally relevant aspects of the state and irrelevant distractors, ... | INFORMATION PRIORITIZATION THROUGH EMPOWERMENT IN VISUAL MODEL-BASED RL |
d219721308 | Determinantal point processes (DPPs) have attracted significant attention from the machine learning community for their ability to model subsets drawn from a large collection of items. Recent work shows that nonsymmetric DPP kernels have significant advantages over symmetric kernels in terms of modeling power and predi... | Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes |
d204734215 | Many tasks in modern machine learning can be formulated as finding equilibria in sequential games. In particular, two-player zero-sum sequential games, also known as minimax optimization, have received growing interest. It is tempting to apply gradient descent to solve minimax optimization given its popularity and succ... | ON SOLVING MINIMAX OPTIMIZATION LOCALLY: A FOLLOW-THE-RIDGE APPROACH |
d231648420 | Facial recognition systems are increasingly deployed by private corporations, government agencies, and contractors for consumer services and mass surveillance programs alike. These systems are typically built by scraping social media profiles for user images. Adversarial perturbations have been proposed for bypassing f... | LOWKEY: LEVERAGING ADVERSARIAL ATTACKS TO PROTECT SOCIAL MEDIA USERS FROM FACIAL RECOGNITION |
d252280608 | Deep learning based approaches like Physics-informed neural networks (PINNs) and DeepONets have shown promise on solving PDE constrained optimization (PDECO) problems. However, existing methods are insufficient to handle those PDE constraints that have a complicated or nonlinear dependency on optimization targets. In... | BI-LEVEL PHYSICS-INFORMED NEURAL NETWORKS FOR PDE CONSTRAINED OPTIMIZATION USING BROYDEN'S HYPERGRADIENTS |
d234790212 | Object goal navigation aims to steer an agent towards a target object based on observations of the agent. It is of pivotal importance to design effective visual representations of the observed scene in determining navigation actions. In this paper, we introduce a Visual Transformer Network (VTNet) for learning informat... | VTNET: VISUAL TRANSFORMER NETWORK FOR OBJECT GOAL NAVIGATION |
d236965836 | Imitation learning algorithms learn a policy from demonstrations of expert behavior. We show that, for deterministic experts, imitation learning can be done by reduction to reinforcement learning with a stationary reward. Our theoretical analysis both certifies the recovery of expert reward and bounds the total variati... | IMITATION LEARNING BY REINFORCEMENT LEARNING |
d247518687 | Capturing aleatoric uncertainty is a critical part of many machine learning systems. In deep learning, a common approach to this end is to train a neural network to estimate the parameters of a heteroscedastic Gaussian distribution by maximizing the logarithm of the likelihood function under the observed data. In this ... | ON THE PITFALLS OF HETEROSCEDASTIC UNCERTAINTY ESTIMATION WITH PROBABILISTIC NEURAL NETWORKS |
d247594344 | Thanks to the power of representation learning, neural contextual bandit algorithms demonstrate remarkable performance improvement against their classical counterparts. But because their exploration has to be performed in the entire neural network parameter space to obtain nearly optimal regret, the resulting computati... | LEARNING NEURAL CONTEXTUAL BANDITS THROUGH PERTURBED REWARDS |
d257833590 | Disentangling complex data to its latent factors of variation is a fundamental task in representation learning. Existing work on sequential disentanglement mostly provides two factor representations, i.e., it separates the data to time-varying and time-invariant factors. In contrast, we consider multifactor disentangle... | MULTIFACTOR SEQUENTIAL DISENTANGLEMENT VIA STRUCTURED KOOPMAN AUTOENCODERS |
d5034059 | For natural language understanding (NLU) technology to be maximally useful, it must be able to process language in a way that is not exclusively tailored to a specific task, genre, or dataset. In pursuit of this objective, we introduce the General Language Understanding Evaluation (GLUE) benchmark, a collection of tool... | GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding |
d220546448 | Although much progress has been made towards robust deep learning, a significant gap in robustness remains between real-world perturbations and more narrowly defined sets typically studied in adversarial defenses. In this paper, we aim to bridge this gap by learning perturbation sets from data, in order to characterize... | Learning perturbation sets for robust machine learning |
d233204603 | We present ReCo, a contrastive learning framework designed at a regional level to assist learning in semantic segmentation. ReCo performs semi-supervised or supervised pixel-level contrastive learning on a sparse set of hard negative pixels, with minimal additional memory footprint. ReCo is easy to implement, being bui... | Bootstrapping Semantic Segmentation with Regional Contrast |
d208202124 | Approaches to continual learning aim to successfully learn a set of related tasks that arrive in an online manner. Recently, several frameworks have been developed which enable deep learning to be deployed in this learning scenario. A key modelling decision is to what extent the architecture should be shared across tas... | Continual Learning with Adaptive Weights (CLAW) |
d257365037 | Important research efforts have focused on the design and training of neural networks with a controlled Lipschitz constant.The goal is to increase and sometimes guarantee the robustness against adversarial attacks.Recent promising techniques draw inspirations from different backgrounds to design 1-Lipschitz neural netw... | A UNIFIED ALGEBRAIC PERSPECTIVE ON LIPSCHITZ NEURAL NETWORKS |
d195798643 | Federated learning enables a large amount of edge computing devices to learn a centralized model while keeping all local data on edge devices. As a leading algorithm in this setting, Federated Averaging (FedAvg) runs Stochastic Gradient Descent (SGD) in parallel on a small subset of the total devices and averages the s... | On the Convergence of FedAvg on Non-IID Data |
d238354021 | We introduce Autoregressive Diffusion Models (ARDMs), a model class encompassing and generalizing order-agnostic autoregressive models(Uria et al., 2014)and absorbing discrete diffusion(Austin et al., 2021), which we show are special cases of ARDMs under mild assumptions. ARDMs are simple to implement and easy to train... | AUTOREGRESSIVE DIFFUSION MODELS |
d218720006 | Mirror descent (MD), a well-known first-order method in constrained convex optimization, has recently been shown as an important tool to analyze trust-region algorithms in reinforcement learning (RL). However, there remains a considerable gap between such theoretically analyzed algorithms and the ones used in practice.... | Mirror Descent Policy Optimization |
d247618912 | Many promising applications of supervised machine learning face hurdles in the acquisition of labeled data in sufficient quantity and quality, creating an expensive bottleneck. To overcome such limitations, techniques that do not depend on ground truth labels have been studied, including weak supervision and generative... | GENERATIVE MODELING HELPS WEAK SUPERVISION (AND VICE VERSA) |
d238857286 | This paper investigates unsupervised learning of Full-Waveform Inversion (FWI), which has been widely used in geophysics to estimate subsurface velocity maps from seismic data. This problem is mathematically formulated by a second order partial differential equation (PDE), but is hard to solve. Moreover, acquiring velo... | UNSUPERVISED LEARNING OF FULL-WAVEFORM INVERSION: CONNECTING CNN AND PARTIAL DIFFERENTIAL EQUATION IN A LOOP |
d259287063 | Neural networks can be significantly compressed by pruning, yielding sparse models with reduced storage and computational demands while preserving predictive performance.Model soups(Wortsman et al., 2022a)enhance generalization and out-of-distribution (OOD) performance by averaging the parameters of multiple models int... | SPARSE MODEL SOUPS: A RECIPE FOR IMPROVED PRUNING VIA MODEL AVERAGING |
d249097923 | The vulnerability of deep neural networks to adversarial examples has drawn tremendous attention from the community. Three approaches, optimizing standard objective functions, exploiting attention maps, and smoothing decision surfaces, are commonly used to craft adversarial examples. By tightly integrating the three ap... | TRANSFERABLE ADVERSARIAL ATTACK BASED ON INTEGRATED GRADIENTS |
d252668422 | We consider two biologically plausible structures, the Spiking Neural Network (SNN) and the self-attention mechanism. The former offers an energy-efficient and event-driven paradigm for deep learning, while the latter has the ability to capture feature dependencies, enabling Transformer to achieve good performance. It ... | SPIKFORMER: WHEN SPIKING NEURAL NETWORK MEETS TRANSFORMER |
d249431867 | We consider the off-policy evaluation problem of reinforcement learning using deep convolutional neural networks. We analyze the deep fitted Q-evaluation method for estimating the expected cumulative reward of a target policy, when the data are generated from an unknown behavior policy. We show that, by choosing networ... | Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks |
d14096841 | We present an approach to training neural networks to generate sequences using actor-critic methods from reinforcement learning (RL). Current log-likelihood training methods are limited by the discrepancy between their training and testing modes, as models must generate tokens conditioned on their previous guesses rath... | AN ACTOR-CRITIC ALGORITHM FOR SEQUENCE PREDICTION |
d209460718 | Data augmentation (DA) has been widely utilized to improve generalization in training deep neural networks. Recently, human-designed data augmentation has been gradually replaced by automatically learned augmentation policy. Through finding the best policy in well-designed search space of data augmentation, Au-toAugmen... | ADVERSARIAL AUTOAUGMENT |
d3525232 | We introduce a new dataset of logical entailments for the purpose of measuring models' ability to capture and exploit the structure of logical expressions against an entailment prediction task. We use this task to compare a series of architectures which are ubiquitous in the sequence-processing literature, in addition ... | CAN NEURAL NETWORKS UNDERSTAND LOGICAL ENTAILMENT? |
d2103669 | This article presents the prediction difference analysis method for visualizing the response of a deep neural network to a specific input. When classifying images, the method highlights areas in a given input image that provide evidence for or against a certain class. It overcomes several shortcoming of previous method... | VISUALIZING DEEP NEURAL NETWORK DECISIONS: PREDICTION DIFFERENCE ANALYSIS |
d220280819 | We introduce Adaptive Procedural Task Generation (APT-Gen), an approach for progressively generating a sequence of tasks as curricula to facilitate reinforcement learning in hard-exploration problems. At the heart of our approach, a task generator learns to create tasks via a black-box procedural generation module by a... | Adaptive Procedural Task Generation for Hard-Exploration Problems |
d249395483 | Past research on interactive decision making problems (bandits, reinforcement learning, etc.) mostly focuses on the minimax regret that measures the algorithm's performance on the hardest instance. However, an ideal algorithm should adapt to the complexity of a particular problem instance and incur smaller regrets on e... | Asymptotic Instance-Optimal Algorithms for Interactive Decision Making |
d3529936 | Implicit models, which allow for the generation of samples but not for point-wise evaluation of probabilities, are omnipresent in real world problems tackled by machine learning and a hot topic of current research. Some examples include data simulators that are widely used in engineering and scientific research, genera... | Gradient Estimators for Implicit Models |
d232478335 | This work aims to empirically clarify a recently discovered perspective that label smoothing is incompatible with knowledge distillation(Müller et al., 2019). We begin by introducing the motivation behind on how this incompatibility is raised, i.e., label smoothing erases relative information between teacher logits. We... | IS LABEL SMOOTHING TRULY INCOMPATIBLE WITH KNOWLEDGE DISTILLATION: AN EMPIRICAL STUDY |
d227254803 | We study a general class of contextual bandits, where each context-action pair is associated with a raw feature vector, but the reward generating function is unknown. We propose a novel learning algorithm that transforms the raw feature vector using the last hidden layer of a deep ReLU neural network (deep representati... | Neural Contextual Bandits with Deep Representation and Shallow Exploration |
d208857488 | A major component of overfitting in model-free reinforcement learning (RL) involves the case where the agent may mistakenly correlate reward with certain spurious features from the observations generated by the Markov Decision Process (MDP). We provide a general framework for analyzing this scenario, which we use to de... | Observational Overfitting in Reinforcement Learning |
d246822414 | The current methods for learning representations with auto-encoders almost exclusively employ vectors as the latent representations. In this work, we propose to employ a tensor product structure for this purpose. This way, the obtained representations are naturally disentangled. In contrast to the conventional variatio... | UNSUPERVISED DISENTANGLEMENT WITH TENSOR PRODUCT REPRESENTATIONS ON THE TORUS |
d245117682 | In many practical applications of RL, it is expensive to observe state transitions from the environment. For example, in the problem of plasma control for nuclear fusion, computing the next state for a given state-action pair requires querying an expensive transition function which can lead to many hours of computer si... | AN EXPERIMENTAL DESIGN PERSPECTIVE ON MODEL-BASED REINFORCEMENT LEARNING |
d256390058 | A temporal point process (TPP) is a stochastic process where its realization is a sequence of discrete events in time. Recent work in TPPs model the process using a neural network in a supervised learning framework, where a training set is a collection of all the sequences. In this work, we propose to train TPPs in a m... | META TEMPORAL POINT PROCESSES |
d67856276 | Multilingual machine translation, which translates multiple languages with a single model, has attracted much attention due to its efficiency of offline training and online serving. However, traditional multilingual translation usually yields inferior accuracy compared with the counterpart using individual models for e... | MULTILINGUAL NEURAL MACHINE TRANSLATION WITH KNOWLEDGE DISTILLATION |
d235293986 | A probabilistic classifier with reliable predictive uncertainties i) fits successfully to the target domain data, ii) provides calibrated class probabilities in difficult regions of the target domain (e.g. class overlap), and iii) accurately identifies queries coming out of the target domain and rejects them. We introd... | EVIDENTIAL TURING PROCESSES |
d6610705 | We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This allows training to be adjusted between using the optimal discriminator in the generator's objective, which is ideal but infeasible in practice... | UNROLLED GENERATIVE ADVERSARIAL NETWORKS |
d3557557 | What makes humans so good at solving seemingly complex video games? Unlike computers, humans bring in a great deal of prior knowledge about the world, enabling efficient decision making. This paper investigates the role of human priors for solving video games. Given a sample game, we conduct a series of ablation studie... | Investigating Human Priors for Playing Video Games |
d264438909 | Exposing meaningful and interpretable neural interactions is critical to understanding neural circuits.Inferred neural interactions from neural signals primarily reflect functional interactions.In a long experiment, subject animals may experience different stages defined by the experiment, stimuli, or behavioral states... | ONE-HOT GENERALIZED LINEAR MODEL FOR SWITCHING BRAIN STATE DISCOVERY |
d235417023 | The adversarial vulnerability of deep neural networks has attracted significant attention in machine learning. From a causal viewpoint, adversarial attacks can be considered as a specific type of distribution change on natural data. As causal reasoning has an instinct for modeling distribution change, we propose to inc... | Adversarial Robustness through the Lens of Causality |
d49473438 | While Generative Adversarial Networks (GANs) have empirically produced impressive results on learning complex real-world distributions, recent work has shown that they suffer from lack of diversity or mode collapse. The theoretical work of Arora et al.[2]suggests a dilemma about GANs' statistical properties: powerful d... | Approximability of Discriminators Implies Diversity in GANs |
d54101493 | Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on... | IMAGENET-TRAINED CNNS ARE BIASED TOWARDS TEXTURE; INCREASING SHAPE BIAS IMPROVES ACCURACY AND ROBUSTNESS |
d56657799 | We present a simple and general method to train a single neural network executable at different widths 1 , permitting instant and adaptive accuracy-efficiency trade-offs at runtime. Instead of training individual networks with different width configurations, we train a shared network with switchable batch normalization... | SLIMMABLE NEURAL NETWORKS |
d254043945 | Existing object detection methods are bounded in a fixed-set vocabulary by costly labeled data. When dealing with novel categories, the model has to be retrained with more bounding box annotations. Natural language supervision is an attractive alternative for its annotation-free attributes and broader object concepts.H... | LEARNING OBJECT-LANGUAGE ALIGNMENTS FOR OPEN-VOCABULARY OBJECT DETECTION |
d252816031 | Meta-training, which fine-tunes the language model (LM) on various downstream tasks by maximizing the likelihood of the target label given the task instruction and input instance, has improved the zero-shot task generalization performance. However, meta-trained LMs still struggle to generalize to challenging tasks cont... | GUESS THE INSTRUCTION! FLIPPED LEARNING MAKES LANGUAGE MODELS STRONGER ZERO-SHOT LEARNERS |
d253707922 | How should we intervene on an unknown structural causal model to maximize a downstream variable of interest? This optimization of the output of a system of interconnected variables, also known as causal Bayesian optimization (CBO), has important applications in medicine, ecology, and manufacturing. Standard Bayesian op... | MODEL-BASED CAUSAL BAYESIAN OPTIMIZATION |
d235377198 | We propose a new framework of synthesizing data using deep generative models in a differentially private manner. Within our framework, sensitive data are sanitized with rigorous privacy guarantees in a one-shot fashion, such that training deep generative models is possible without re-using the original data. Hence, no ... | PEARL: DATA SYNTHESIS VIA PRIVATE EMBEDDINGS AND ADVERSARIAL RECONSTRUCTION LEARNING |
d236912505 | Reasoning about the future -understanding how decisions in the present time affect outcomes in the future -is one of the central challenges for reinforcement learning (RL), especially in highly-stochastic or partially observable environments. While predicting the future directly is hard, in this work we introduce a met... | Policy Gradients Incorporating the Future |
d261823054 | Markov processes are widely used mathematical models for describing dynamic systems in various fields.However, accurately simulating large-scale systems at long time scales is computationally expensive due to the short time steps required for accurate integration.In this paper, we introduce an inference process that ma... | Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck |
d225067567 | We re-evaluate the standard practice of sharing weights between input and output embeddings in state-of-the-art pre-trained language models. We show that decoupled embeddings provide increased modeling flexibility, allowing us to significantly improve the efficiency of parameter allocation in the input embedding of mul... | RETHINKING EMBEDDING COUPLING IN PRE-TRAINED LANGUAGE MODELS |
d256358895 | Although many methods have been proposed to estimate attributions of input variables, there still exists a significant theoretical flaw in masking-based attribution methods, i.e. it is hard to examine whether the masking method faithfully represents the absence of input variables. Specifically, for masking-based attrib... | CAN WE FAITHFULLY REPRESENT ABSENCE STATES TO COMPUTE SHAPLEY VALUES ON A DNN? |
d259108558 | In many domains, autoregressive models can attain high likelihood on the task of predicting the next observation. However, this maximum-likelihood (MLE) objective does not necessarily match a downstream use-case of autoregressively generating high-quality sequences. The MLE objective weights sequences proportionally to... | SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking |
d211003696 | Markov Logic Networks (MLNs), which elegantly combine logic rules and probabilistic graphical models, can be used to address many knowledge graph problems. However, inference in MLN is computationally intensive, making the industrialscale application of MLN very difficult. In recent years, graph neural networks (GNNs) ... | EFFICIENT PROBABILISTIC LOGIC REASONING WITH GRAPH NEURAL NETWORKS |
d259064354 | Motivated by social network analysis and network-based recommendation systems, we study a semi-supervised community detection problem in which the objective is to estimate the community label of a new node using the network topology and partially observed community labels of existing nodes. The network is modeled using... | SEMI-SUPERVISED COMMUNITY DETECTION VIA STRUCTURAL SIMILARITY METRICS |
d252967887 | The low-level sensory and motor signals in deep reinforcement learning, which exist in high-dimensional spaces such as image observations or motor torques, are inherently challenging to understand or utilize directly for downstream tasks. While sensory representations have been extensively studied, the representations ... | SIMPLE EMERGENT ACTION REPRESENTATIONS FROM MULTI-TASK POLICY TRAINING |
d249538510 | Despite recent progress in generative adversarial network (GAN)-based vocoders, where the model generates raw waveform conditioned on acoustic features, it is challenging to synthesize high-fidelity audio for numerous speakers across various recording environments. In this work, we present BigVGAN, a universal vocoder ... | BIGVGAN: A UNIVERSAL NEURAL VOCODER WITH LARGE-SCALE TRAINING |
d258331519 | In this paper, we define, evaluate, and improve the "relay-generalization" performance of reinforcement learning (RL) agents on the out-of-distribution "controllable" states. Ideally, an RL agent that generally masters a task should reach its goal starting from any controllable state of the environment instead of memor... | CAN AGENTS RUN RELAY RACE WITH STRANGERS? GENERALIZATION OF RL TO OUT-OF-DISTRIBUTION TRAJECTORIES |
d227346855 | The objective of lifelong reinforcement learning (RL) is to optimize agents which can continuously adapt and interact in changing environments. However, current RL approaches fail drastically when environments are non-stationary and interactions are non-episodic. We propose Lifelong Skill Planning (LiSP), an algorithmi... | RESET-FREE LIFELONG LEARNING WITH SKILL-SPACE PLANNING |
d257767456 | Conditional neural processes (CNPs; Garnelo et al., 2018a) are attractive metalearning models which produce well-calibrated predictions and are trainable via a simple maximum likelihood procedure. Although CNPs have many advantages, they are unable to model dependencies in their predictions. Various works propose solu... | AUTOREGRESSIVE CONDITIONAL NEURAL PROCESSES |
d220302636 | Multimodal learning for generative models often refers to the learning of abstract concepts from the commonality of information in multiple modalities, such as vision and language. While it has proven effective for learning generalisable representations, the training of such models often requires a large amount of "rel... | Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models |
d252716080 | We present a general framework for evolutionary learning to emergent unbiased state representation without any supervision. Evolutionary frameworks such as self-play converge to bad local optima in case of multi-agent reinforcement learning in non-cooperative partially observable environments with communication due to ... | TRUTHFUL SELF-PLAY |
d257279944 | A Capoeira practice. One is kicking and the other is avoiding the kick."Recent work has demonstrated the significant potential of denoising diffusion models for generating human motion, including text-to-motion capabilities. However, these methods are restricted by the paucity of annotated motion data, a focus on singl... | Human Motion Diffusion as a Generative Prior |
d237353080 | Deep neural network (DNN) model compression for efficient on-device inference becomes increasingly important to reduce memory requirements and keep user data on-device. To this end, we propose a novel differentiable k-means clustering layer (DKM) and its application to train-time weight-clustering for DNN model compres... | DKM: DIFFERENTIABLE k-MEANS CLUSTERING LAYER FOR NEURAL NETWORK COMPRESSION |
d204008396 | Learning multilingual representations of text has proven a successful method for many cross-lingual transfer learning tasks. There are two main paradigms for learning such representations: (1) alignment, which maps different independently trained monolingual representations into a shared space, and (2) joint training, ... | CROSS-LINGUAL ALIGNMENT VS JOINT TRAINING: A COMPARATIVE STUDY AND A SIMPLE UNIFIED FRAMEWORK |
d219708931 | Recent studies have shown that skeletonization (pruning parameters) of networks at initialization provides all the practical benefits of sparsity both at inference and training time, while only marginally degrading their performance. However, we observe that beyond a certain level of sparsity (approx 95%), these approa... | Progressive Skeletonization: Trimming more fat from a network at initialization |
d249191769 | While deep learning has outperformed other methods for various tasks, theoretical frameworks that explain its reason have not been fully established. To address this issue, we investigate the excess risk of two-layer ReLU neural networks in a teacher-student regression model, in which a student network learns an unknow... | Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods |
d256598122 | Transfer learning is known to perform efficiently in many applications empirically, yet limited literature reports the mechanism behind the scene. This study establishes both formal derivations and heuristic analysis to formulate the theory of transfer learning in deep learning. Our framework utilizing layer variationa... | INTERPRETATIONS OF DOMAIN ADAPTATIONS VIA LAYER VARIATIONAL ANALYSIS |
d203592088 | Understanding three-dimensional (3D) geometries from two-dimensional (2D) images without any labeled information is promising for understanding the real world without incurring annotation cost. We herein propose a novel generative model, RGBD-GAN, which achieves unsupervised 3D representation learning from 2D images. T... | RGBD-GAN: UNSUPERVISED 3D REPRESENTATION LEARNING FROM NATURAL IMAGE DATASETS VIA RGBD IMAGE SYNTHESIS |
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