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d182953182 | A key component of most neural network architectures is the use of normalization layers, such as Batch Normalization. Despite its common use and large utility in optimizing deep architectures, it has been challenging both to generically improve upon Batch Normalization and to understand the circumstances that lend them... | Published as a conference paper at ICLR 2020 FOUR THINGS EVERYONE SHOULD KNOW TO IMPROVE BATCH NORMALIZATION |
d257219743 | Tackling unfairness in graph learning models is a challenging task, as the unfairness issues on graphs involve both attributes and topological structures. Existing work on fair graph learning simply assumes that attributes of all nodes are available for model training and then makes fair predictions. In practice, howev... | Published as a conference paper at ICLR 2023 FAIR ATTRIBUTE COMPLETION ON GRAPH WITH MISSING ATTRIBUTES |
d244130146 | Reconstructing medical images from partial measurements is an important inverse problem in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Existing solutions based on machine learning typically train a model to directly map measurements to medical images, leveraging a training dataset of paired images an... | Published as a conference paper at ICLR 2022 SOLVING INVERSE PROBLEMS IN MEDICAL IMAGING WITH SCORE-BASED GENERATIVE MODELS |
d3464537 | Deep latent variable models are powerful tools for representation learning. In this paper, we adopt the deep information bottleneck model, identify its shortcomings and propose a model that circumvents them. To this end, we apply a copula transformation which, by restoring the invariance properties of the information b... | Published as a conference paper at ICLR 2018 LEARNING SPARSE LATENT REPRESENTATIONS WITH THE DEEP COPULA INFORMATION BOTTLENECK |
d257833684 | Contrastive learning methods train visual encoders by comparing views (e.g., often created via a group of data augmentations on the same instance) from one instance to others. Typically, the views created from one instance are set as positive, while views from other instances are negative. This binary instance discrimi... | Published as a conference paper at ICLR 2023 SOFT NEIGHBORS ARE POSITIVE SUPPORTERS IN CONTRASTIVE VISUAL REPRESENTATION LEARNING |
d252715605 | Clustering algorithms are widely used in many societal resource allocation applications, such as loan approvals and candidate recruitment, among others, and hence, biased or unfair model outputs can adversely impact individuals that rely on these applications. To this end, many fair clustering approaches have been rece... | Published as a conference paper at ICLR 2023 ROBUST FAIR CLUSTERING: A NOVEL FAIRNESS ATTACK AND DEFENSE FRAMEWORK |
d226282371 | The existing Neural ODE formulation relies on an explicit knowledge of the termination time. We extend Neural ODEs to implicitly defined termination criteria modeled by neural event functions, which can be chained together and differentiated through. Neural Event ODEs are capable of modeling discrete and instantaneous ... | Published as a conference paper at ICLR 2021 LEARNING NEURAL EVENT FUNCTIONS FOR ORDINARY DIFFERENTIAL EQUATIONS |
d249151922 | Graph neural networks (GNNs) continue to achieve state-of-the-art performance on many graph learning tasks, but rely on the assumption that a given graph is a sufficient approximation of the true neighborhood structure. When a system contains higher-order sequential dependencies, we show that the tendency of traditiona... | Published as a conference paper at ICLR 2023 DEEP ENSEMBLES FOR GRAPHS WITH HIGHER-ORDER DEPENDENCIES |
d232404824 | Can deep learning solve multiple tasks simultaneously, even when they are unrelated and very different? We investigate how the representations of the underlying tasks affect the ability of a single neural network to learn them jointly. We present theoretical and empirical findings that a single neural network is capabl... | Published as a conference paper at ICLR 2021 ONE NETWORK FITS ALL? MODULAR VERSUS MONOLITHIC TASK FORMULATIONS IN NEURAL NETWORKS |
d12734615 | The policy gradients of the expected return objective can react slowly to rare rewards. Yet, in some cases agents may wish to emphasize the low or high returns regardless of their probability. Borrowing from the economics and control literature, we review the risk-sensitive value function that arises from an exponentia... | Workshop track -ICLR 2017 PARTICLE VALUE FUNCTIONS |
d211021032 | The question of whether deep neural networks are good at generalising beyond their immediate training experience is of critical importance for learning-based approaches to AI. Here, we consider tests of out-of-sample generalisation that require an agent to respond to never-seen-before instructions by manipulating and p... | ENVIRONMENTAL DRIVERS OF SYSTEMATICITY AND GENERALISATION IN A SITUATED AGENT |
d233024779 | Though convolutional neural networks (CNNs) have demonstrated remarkable ability in learning discriminative features, they often generalize poorly to unseen domains. Domain generalization aims to address this problem by learning from a set of source domains a model that is generalizable to any unseen domain. In this pa... | Published as a conference paper at ICLR 2021 DOMAIN GENERALIZATION WITH MIXSTYLE |
d3329074 | In this paper, we propose an interpretable LSTM recurrent neural network, i.e., multi-variable LSTM for time series with exogenous variables. Currently, widely used attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance. To th... | Workshop track -ICLR 2018 AN INTERPRETABLE LSTM NEURAL NETWORK FOR AUTOREGRESSIVE EXOGENOUS MODEL |
d252780848 | Inverse graphics aims to recover 3D models from 2D observations. Utilizing differentiable rendering, recent 3D-aware generative models have shown impressive results of rigid object generation using 2D images. However, it remains challenging to generate articulated objects, like human bodies, due to their complexity and... | EVA3D: COMPOSITIONAL 3D HUMAN GENERATION FROM 2D IMAGE COLLECTIONS Figure 1: EVA3D generates high-quality and diverse 3D humans with photo-realistic RGB render- ings and detailed geometry. Only 2D image collections are used for training |
d258236460 | Deep reinforcement learning algorithms that learn policies by trial-and-error must learn from limited amounts of data collected by actively interacting with the environment. While many prior works have shown that proper regularization techniques are crucial for enabling data-efficient RL, a general understanding of the... | Published as a conference paper at ICLR 2023 EFFICIENT DEEP REINFORCEMENT LEARNING REQUIRES REGULATING OVERFITTING |
d248496160 | State-of-the-art neural network verifiers are fundamentally based on one of two paradigms: either encoding the whole verification problem via tight multi-neuron convex relaxations or applying a Branch-and-Bound (BaB) procedure leveraging imprecise but fast bounding methods on a large number of easier subproblems. The f... | Published as a conference paper at ICLR 2022 COMPLETE VERIFICATION VIA MULTI-NEURON RELAXATION GUIDED BRANCH-AND-BOUND |
d3273601 | Disentangling factors of variation has always been a challenging problem in representation learning. Existing algorithms suffer from many limitations, such as unpredictable disentangling factors, bad quality of generated images from encodings, lack of identity information, etc. In this paper, we propose a supervised al... | DNA-GAN: LEARNING DISENTANGLED REPRESEN- TATIONS FROM MULTI-ATTRIBUTE IMAGES |
d2926851 | We describe a new class of learning models called memory networks. Memory networks reason with inference components combined with a long-term memory component; they learn how to use these jointly. The long-term memory can be read and written to, with the goal of using it for prediction. We investigate these models in t... | Memory Networks |
d221761146 | Pre-trained models for programming language have achieved dramatic empirical improvements on a variety of code-related tasks such as code search, code completion, code summarization, etc. However, existing pre-trained models regard a code snippet as a sequence of tokens, while ignoring the inherent structure of code, w... | Published as a conference paper at ICLR 2021 GRAPHCODEBERT: PRE-TRAINING CODE REPRESEN- TATIONS WITH DATA FLOW |
d17682909 | Convolutional Neural Networks are extremely efficient architectures in image and audio recognition tasks, thanks to their ability to exploit the local translational invariance of signal classes over their domain. In this paper we consider possible generalizations of CNNs to signals defined on more general domains witho... | Spectral Networks and Deep Locally Connected Networks on Graphs |
d8895303 | We consider whether deep convolutional networks (CNNs) can represent decision functions with similar accuracy as recurrent networks such as LSTMs. First, we show that a deep CNN with an architecture inspired by the models recently introduced in image recognition can yield better accuracy than previous convolutional and... | Workshop track -ICLR 2016 BLENDING LSTMS INTO CNNS |
d249889348 | When deploying Reinforcement Learning (RL) agents into a physical system, we must ensure that these agents are well aware of the underlying constraints. In many real-world problems, however, the constraints are often hard to specify mathematically and unknown to the RL agents. To tackle these issues, Inverse Constraine... | Published as a conference paper at ICLR 2023 BENCHMARKING CONSTRAINT INFERENCE IN IN- VERSE REINFORCEMENT LEARNING |
d247447758 | The discovery of sparse subnetworks that are able to perform as well as full models has found broad applied and theoretical interest. While many pruning methods have been developed to this end, the naïve approach of removing parameters based on their magnitude has been found to be as robust as more complex, state-of-th... | Published as a conference paper at ICLR 2022 AN OPERATOR THEORETIC VIEW ON PRUNING DEEP NEURAL NETWORKS |
d256105701 | The large number of ReLU non-linearity operations in existing deep neural networks makes them ill-suited for latency-efficient private inference (PI). Existing techniques to reduce ReLU operations often involve manual effort and sacrifice significant accuracy. In this paper, we first present a novel measure of non-line... | Published as a conference paper at ICLR 2023 LEARNING TO LINEARIZE DEEP NEURAL NETWORKS FOR SECURE AND EFFICIENT PRIVATE INFERENCE |
d256900618 | Diffusion probabilistic models (DPMs) have become a popular approach to conditional generation, due to their promising results and support for cross-modal synthesis. A key desideratum in conditional synthesis is to achieve high correspondence between the conditioning input and generated output. Most existing methods le... | DISCRETE CONTRASTIVE DIFFUSION FOR CROSS- MODAL MUSIC AND IMAGE GENERATION |
d203902511 | Modern deep learning methods provide effective means to learn good representations. However, is a good representation itself sufficient for sample efficient reinforcement learning? This question has largely been studied only with respect to (worst-case) approximation error, in the more classical approximate dynamic pro... | Published as a conference paper at ICLR 2020 IS A GOOD REPRESENTATION SUFFICIENT FOR SAM- PLE EFFICIENT REINFORCEMENT LEARNING? |
d232269768 | In recent years, Generative Adversarial Networks have become ubiquitous in both research and public perception, but how GANs convert an unstructured latent code to a high quality output is still an open question. In this work, we investigate regression into the latent space as a probe to understand the compositional pr... | Published as a conference paper at ICLR 2021 USING LATENT SPACE REGRESSION TO ANALYZE AND LEVERAGE COMPOSITIONALITY IN GANS |
d257482853 | Recurrent neural networks (RNNs) are well suited for solving sequence tasks in resource-constrained systems due to their expressivity and low computational requirements. However, there is still a need to bridge the gap between what RNNs are capable of in terms of efficiency and performance and real-world application re... | Published as a conference paper at ICLR 2023 EFFICIENT RECURRENT ARCHITECTURES THROUGH ACTIVITY SPARSITY AND SPARSE BACK-PROPAGATION THROUGH TIME |
d257102428 | Physical simulations that accurately model reality are crucial for many engineering disciplines such as mechanical engineering and robotic motion planning. In recent years, learned Graph Network Simulators produced accurate mesh-based simulations while requiring only a fraction of the computational cost of traditional ... | Published as a conference paper at ICLR 2023 GROUNDING GRAPH NETWORK SIMULATORS USING PHYSICAL SENSOR OBSERVATIONS |
d257364759 | This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms. Our studies discover a new attack pattern that negatively impact the forecasting of a target time series via making strategic, sparse (imperceptible) modifications to the past observations... | Published as a conference paper at ICLR 2023 ROBUST MULTIVARIATE TIME-SERIES FORECASTING: ADVERSARIAL ATTACKS AND DEFENSE MECHANISMS |
d231807280 | The recent paper byByrd & Lipton (2019), based on empirical observations, raises a major concern on the impact of importance weighting for the over-parameterized deep learning models. They observe that as long as the model can separate the training data, the impact of importance weighting diminishes as the training pro... | UNDERSTANDING THE ROLE OF IMPORTANCE WEIGHT- ING FOR DEEP LEARNING |
d208637407 | Modern deep neural networks can achieve high accuracy when the training distribution and test distribution are identically distributed, but this assumption is frequently violated in practice. When the train and test distributions are mismatched, accuracy can plummet. Currently there are few techniques that improve robu... | Published as a conference paper at ICLR 2020 AUGMIX: A SIMPLE DATA PROCESSING METHOD TO IMPROVE ROBUSTNESS AND UNCERTAINTY |
d232185174 | Several works have shown that the regularization mechanisms underlying deep neural networks' generalization performances are still poorly understood(Neyshabur et al., 2015;Zhang et al., 2017). In this paper, we hypothesize that deep neural networks are regularized through their ability to extract meaningful clusters am... | Published as a conference paper at ICLR 2021 INTRACLASS CLUSTERING: AN IMPLICIT LEARNING ABILITY THAT REGULARIZES DNNS |
d53116133 | Words are not created equal. In fact, they form an aristocratic graph with a latent hierarchical structure that the next generation of unsupervised learned word embeddings should reveal. In this paper, justified by the notion of delta-hyperbolicity or tree-likeliness of a space, we propose to embed words in a Cartesian... | POINCARÉ GLOVE: HYPERBOLIC WORD EMBEDDINGS |
d253238033 | Fully-parametric language models generally require a huge number of model parameters to store the necessary knowledge for solving multiple natural language tasks in zero/few-shot settings. In addition, it is hard to adapt to the evolving world knowledge without the costly model re-training. In this paper, we develop a ... | KNOWLEDGE-IN-CONTEXT: TOWARDS KNOWLEDGE- ABLE SEMI-PARAMETRIC LANGUAGE MODELS |
d256697616 | Random-feature-based attention (RFA) is an efficient approximation of softmax attention with linear runtime and space complexity. However, the approximation gap between RFA and conventional softmax attention is not well studied. Built upon previous progress of RFA, we characterize this gap through the lens of control v... | Published as a conference paper at ICLR 2023 EFFICIENT ATTENTION VIA CONTROL VARIATES |
d237605600 | This article considers the popular MCMC method of unadjusted Langevin Monte Carlo (LMC) and provides a non-asymptotic analysis of its sampling error in 2-Wasserstein distance. The proof is based on a refinement of mean-square analysis in Li et al.(2019), and this refined framework automates the analysis of a large clas... | Published as a conference paper at ICLR 2022 SQRT(D) DIMENSION DEPENDENCE OF LANGEVIN MONTE CARLO |
d232335748 | Concentration of measure has been argued to be the fundamental cause of adversarial vulnerability. Mahloujifar et al. (2019b) presented an empirical way to measure the concentration of a data distribution using samples, and employed it to find lower bounds on intrinsic robustness for several benchmark datasets. Howeve... | IMPROVED ESTIMATION OF CONCENTRATION UNDER p -NORM DISTANCE METRICS USING HALF SPACES |
d210713887 | This paper proposes the use of spectral element methods(Canuto et al., 1988)for fast and accurate training of Neural Ordinary Differential Equations (ODE-Nets;Chen et al., 2018). This is achieved by expressing their dynamics as truncated series of Legendre polynomials. The series coefficients, as well as the network we... | Accelerating Neural ODEs with Spectral Elements |
d257353697 | Neural ordinary differential equations (Neural ODEs) are an effective framework for learning dynamical systems from irregularly sampled time series data. These models provide a continuous-time latent representation of the underlying dynamical system where new observations at arbitrary time points can be used to update ... | Published as a conference paper at ICLR 2023 ANAMNESIC NEURAL DIFFERENTIAL EQUATIONS WITH ORTHOGONAL POLYNOMIALS PROJECTIONS |
d256390313 | In the area of few-shot anomaly detection (FSAD), efficient visual feature plays an essential role in the memory bank M-based methods.However, these methods do not account for the relationship between the visual feature and its rotated visual feature, drastically limiting the anomaly detection performance.To push the l... | PUSHING THE LIMITS OF FEW-SHOT ANOMALY DE-TECTION IN INDUSTRY VISION: GRAPHCORE |
d249538446 | The generalization of model-based reinforcement learning (MBRL) methods to environments with unseen transition dynamics is an important yet challenging problem.Existing methods try to extract environment-specified information Z from past transition segments to make the dynamics prediction model generalizable to differe... | A RELATIONAL INTERVENTION APPROACH FOR UN-SUPERVISED DYNAMICS GENERALIZATION IN MODEL-BASED REINFORCEMENT LEARNING |
d232135338 | The goal of the paper is to design active learning strategies which lead to domain adaptation under an assumption of Lipschitz functions. Building on previous work by Mansour et al. (2009) we adapt the concept of discrepancy distance between source and target distributions to restrict the maximization over the hypothe... | DISCREPANCY-BASED ACTIVE LEARNING FOR DO- MAIN ADAPTATION |
d257205872 | Recently, generalization on out-of-distribution (OOD) data with correlation shift has attracted great attentions. The correlation shift is caused by the spurious attributes that correlate to the class label, as the correlation between them may vary in training and test data. For such a problem, we show that given the c... | Published as a conference paper at ICLR 2023 BREAKING CORRELATION SHIFT VIA CONDITIONAL INVARIANT REGULARIZER |
d256826746 | While multi-agent trust region algorithms have achieved great success empirically in solving coordination tasks, most of them, however, suffer from a nonstationarity problem since agents update their policies simultaneously. In contrast, a sequential scheme that updates policies agent-by-agent provides another perspect... | Published as a conference paper at ICLR 2023 ORDER MATTERS: AGENT-BY-AGENT POLICY OPTI- MIZATION |
d244709097 | Numerous physical systems are described by ordinary or partial differential equations whose solutions are given by holomorphic or meromorphic functions in the complex domain. In many cases, only the magnitude of these functions are observed on various points on the purely imaginary jω-axis since coherent measurement of... | BLASCHKE PRODUCT NEURAL NETWORK (BPNN): A PHYSICS-INFUSED NEURAL NETWORK FOR PHASE RETRIEVAL OF MEROMORPHIC FUNCTIONS |
d10278413 | Kernel canonical correlation analysis (KCCA) is a nonlinear multi-view representation learning technique with broad applicability in statistics and machine learning. Although there is a closed-form solution for the KCCA objective, it involves solving an N × N eigenvalue system where N is the training set size, making i... | LARGE-SCALE APPROXIMATE KERNEL CANONICAL CORRELATION ANALYSIS |
d252907554 | Sequence-to-Sequence (seq2seq) tasks transcribe the input sequence to a target sequence. The Connectionist Temporal Classification (CTC) criterion is widely used in multiple seq2seq tasks. Besides predicting the target sequence, a side product of CTC is to predict the alignment, which is the most probable input-long se... | Published as a conference paper at ICLR 2023 BAYES RISK CTC: CONTROLLABLE CTC ALIGNMENT IN SEQUENCE-TO-SEQUENCE TASKS |
d257404839 | In recent years, contrastive learning achieves impressive results on self-supervised visual representation learning, but there still lacks a rigorous understanding of its learning dynamics. In this paper, we show that if we cast a contrastive objective equivalently into the feature space, then its learning dynamics adm... | A MESSAGE PASSING PERSPECTIVE ON LEARNING DY- NAMICS OF CONTRASTIVE LEARNING |
d257219304 | It's a meaningful and attractive topic to build a general and inclusive segmentation model that can recognize more categories in various scenarios. A straightforward way is to combine the existing fragmented segmentation datasets and train a multidataset network. However, there are two major issues with multi-dataset s... | Published as a conference paper at ICLR 2023 LMSEG: LANGUAGE-GUIDED MULTI-DATASET SEGMENTATION |
d249192149 | Distilling from the feature maps can be fairly effective for dense prediction tasks since both the feature discriminability and localization priors can be well transferred. However, not every pixel contributes equally to the performance, and a good student should learn from what really matters to the teacher. In this p... | Published as a conference paper at ICLR 2023 MASKED DISTILLATION WITH RECEPTIVE TOKENS |
d247613032 | 1We study COMP-AMS, a distributed optimization framework based on gradient averaging and adaptive AMSGrad algorithm. Gradient compression with error feedback is applied to reduce the communication cost in the gradient transmission process. Our convergence analysis of COMP-AMS shows that such compressed gradient averagi... | Published as a conference paper at ICLR 2022 ON DISTRIBUTED ADAPTIVE OPTIMIZATION WITH GRADIENT COMPRESSION |
d210861217 | Modelling highly multi-modal data is a challenging problem in machine learning. Most algorithms are based on maximizing the likelihood, which corresponds to the M(oment)-projection of the data distribution to the model distribution. The Mprojection forces the model to average over modes it cannot represent. In contrast... | Published as a conference paper at ICLR 2020 EXPECTED INFORMATION MAXIMIZATION USING THE I-PROJECTION FOR MIXTURE DENSITY ESTIMATION |
d233231739 | Learning to predict the long-term future of video frames is notoriously challenging due to inherent ambiguities in the distant future and dramatic amplifications of prediction error through time. Despite the recent advances in the literature, existing approaches are limited to moderately short-term prediction (less tha... | Published as a conference paper at ICLR 2021 REVISITING HIERARCHICAL APPROACH FOR PERSISTENT LONG-TERM VIDEO PREDICTION |
d252917944 | Machine learning models are increasingly used in high-stakes decision-making systems. In such applications, a major concern is that these models sometimes discriminate against certain demographic groups such as individuals with certain race, gender, or age. Another major concern in these applications is the violation o... | Published as a conference paper at ICLR 2023 STOCHASTIC DIFFERENTIALLY PRIVATE AND FAIR LEARNING |
d14612342 | We address the problem of contour detection via per-pixel classifications of edge point. To facilitate the process, the proposed approach leverages with DenseNet, an efficient implementation of multiscale convolutional neural networks (CNNs), to extract an informative feature vector for each pixel and uses an SVM class... | PIXEL-WISE DEEP LEARNING FOR CONTOUR DETEC- TION |
d3517962 | This work adopts the very successful distributional perspective on reinforcement learning and adapts it to the continuous control setting. We combine this within a distributed framework for off-policy learning in order to develop what we call the Distributed Distributional Deep Deterministic Policy Gradient algorithm, ... | Published as a conference paper at ICLR 2018 DISTRIBUTED DISTRIBUTIONAL DETERMINISTIC POLICY GRADIENTS |
d245123899 | Humans use natural language to compose common concepts from their environment into plausible, day-to-day scene descriptions. However, such generative commonsense reasoning (GCSR) skills are lacking in state-of-the-art text generation methods. Descriptive sentences about arbitrary concepts generated by neural text gener... | Published as a conference paper at ICLR 2022 CONTEXTUALIZED SCENE IMAGINATION FOR GENERATIVE COMMONSENSE REASONING |
d3506178 | As neural networks grow deeper and wider, learning networks with hard-threshold activations is becoming increasingly important, both for network quantization, which can drastically reduce time and energy requirements, and for creating large integrated systems of deep networks, which may have non-differentiable componen... | DEEP LEARNING AS A MIXED CONVEX- COMBINATORIAL OPTIMIZATION PROBLEM |
d51780574 | Deep generative models provide a systematic way to learn nonlinear data distributions through a set of latent variables and a nonlinear "generator" function that maps latent points into the input space. The nonlinearity of the generator implies that the latent space gives a distorted view of the input space. Under mild... | Published as a conference paper at ICLR 2018 LATENT SPACE ODDITY: ON THE CURVATURE OF DEEP GENERATIVE MODELS |
d11212020 | Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural ma... | Published as a conference paper at ICLR 2015 NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE |
d247582435 | Recent work explored the potential of large-scale Transformer-based pre-trained models, especially Pre-trained Language Models (PLMs) in natural language processing. This raises many concerns from various perspectives, e.g., financial costs and carbon emissions. Compressing PLMs like BERT with negligible performance lo... | Published as a conference paper at ICLR 2022 EXPLORING EXTREME PARAMETER COMPRESSION FOR PRE-TRAINED LANGUAGE MODELS |
d257220165 | Denoising diffusion models are a popular class of generative models providing state-of-the-art results in many domains. One adds gradually noise to data using a diffusion to transform the data distribution into a Gaussian distribution. Samples from the generative model are then obtained by simulating an approximation o... | Published as a conference paper at ICLR 2023 DENOISING DIFFUSION SAMPLERS |
d17280075 | Two potential bottlenecks on the expressiveness of recurrent neural networks (RNNs) are their ability to store information about the task in their parameters, and to store information about the input history in their units. We show experimentally that all common RNN architectures achieve nearly the same per-task and pe... | Published as a conference paper at ICLR 2017 CAPACITY AND TRAINABILITY IN RECURRENT NEURAL NETWORKS |
d29842525 | Topic models are one of the most popular methods for learning representations of text, but a major challenge is that any change to the topic model requires mathematically deriving a new inference algorithm. A promising approach to address this problem is autoencoding variational Bayes (AEVB), but it has proven difficul... | Published as a conference paper at ICLR 2017 AUTOENCODING VARIATIONAL INFERENCE FOR TOPIC MODELS |
d246430723 | The exponential growth in numbers of parameters of neural networks over the past years has been accompanied by an increase in performance across several fields. However, due to their sheer size, the networks not only became difficult to interpret but also problematic to train and use in real-world applications, since h... | Published as a conference paper at ICLR 2022 SIGNING THE SUPERMASK: KEEP, HIDE, INVERT |
d16231549 | Suitable lateral connections between encoder and decoder are shown to allow higher layers of a denoising autoencoder (dAE) to focus on invariant representations. In regular autoencoders, detailed information needs to be carried through the highest layers but lateral connections from encoder to decoder relieve this pres... | Under review as a conference paper at ICLR 2015 DENOISING AUTOENCODER WITH MODULATED LATERAL CONNECTIONS LEARNS INVARIANT REPRESENTATIONS OF NATURAL IMAGES |
d8737624 | In this paper, we propose a framework for training multiple neural networks simultaneously. The parameters from all models are regularised by the tensor trace norm, so that one neural network is encouraged to reuse others' parameters if possible -this is the main motivation behind multi-task learning. In contrast to ma... | Trace Norm Regularised Deep Multi-Task Learning |
d249538418 | The Strong Lottery Ticket Hypothesis (SLTH) stipulates the existence of a subnetwork within a sufficiently overparameterized (dense) neural network that-when initialized randomly and without any training-achieves the accuracy of a fully trained target network. Recent works by da Cunha et al. (2022b); Burkholz (2022a) d... | A GENERAL FRAMEWORK FOR PROVING THE EQUIV- ARIANT STRONG LOTTERY TICKET HYPOTHESIS |
d249394837 | Long-term engagement is preferred over immediate engagement in sequential recommendation as it directly affects product operational metrics such as daily active users (DAUs) and dwell time. Meanwhile, reinforcement learning (RL) is widely regarded as a promising framework for optimizing long-term engagement in sequenti... | Published as a conference paper at ICLR 2023 RESACT: REINFORCING LONG-TERM ENGAGEMENT IN SEQUENTIAL RECOMMENDATION WITH RESIDUAL ACTOR |
d604334 | Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we ... | Intriguing properties of neural networks |
d222125277 | Entities are at the center of how we represent and aggregate knowledge. For instance, Encyclopedias such as Wikipedia are structured by entities (e.g., one per Wikipedia article). The ability to retrieve such entities given a query is fundamental for knowledge-intensive tasks such as entity linking and open-domain ques... | Under review AUTOREGRESSIVE ENTITY RETRIEVAL |
d201668203 | Short-and-sparse deconvolution (SaSD) is the problem of extracting localized, recurring motifs in signals with spatial or temporal structure. Variants of this problem arise in applications such as image deblurring, microscopy, neural spike sorting, and more. The problem is challenging in both theory and practice, as na... | Short-and-Sparse Deconvolution -A Geometric Approach |
d12730344 | Catastrophic forgetting is a problem faced by many machine learning models and algorithms. When trained on one task, then trained on a second task, many machine learning models "forget" how to perform the first task. This is widely believed to be a serious problem for neural networks. Here, we investigate the extent to... | An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks |
d166228758 | We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties, being interpretable, applicable withou... | Published as a conference paper at ICLR 2020 N-BEATS: NEURAL BASIS EXPANSION ANALYSIS FOR INTERPRETABLE TIME SERIES FORECASTING |
d15654042 | In this work we study the properties of deep neural networks with random weights. We formally prove that these networks perform a distance-preserving embedding of the data. Based on this we then draw conclusions on the size of the training data and the networks' structure. | ON THE STABILITY OF DEEP NETWORKS |
d22090507 | We introduce a new audio processing technique that increases the sampling rate of signals such as speech or music using deep convolutional neural networks. Our model is trained on pairs of low and high-quality audio examples; at test-time, it predicts missing samples within a low-resolution signal in an interpolation p... | Workshop track -ICLR 2017 AUDIO SUPER-RESOLUTION USING NEURAL NETS |
d249097686 | Real-world applications require the classification model to adapt to new classes without forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a model with limited memory size to meet this requirement. Typical CIL methods tend to save representative exemplars from former classes to resist... | A MODEL OR 603 EXEMPLARS: TOWARDS MEMORY- EFFICIENT CLASS-INCREMENTAL LEARNING |
d231662264 | Batch Normalization is a key component in almost all state-of-the-art image classifiers, but it also introduces practical challenges: it breaks the independence between training examples within a batch, can incur compute and memory overhead, and often results in unexpected bugs. Building on recent theoretical analyses ... | Published as a conference paper at ICLR 2021 CHARACTERIZING SIGNAL PROPAGATION TO CLOSE THE PERFORMANCE GAP IN UNNORMALIZED RESNETS |
d4394853 | Workshop track -ICLR 2018 META-LEARNING A DYNAMICAL LANGUAGE MODEL | |
d251554799 | Antibody design is valuable for therapeutic usage and biological research. Existing deep-learning-based methods encounter several key issues: 1) incomplete context for Complementarity-Determining Regions (CDRs) generation; 2) incapability of capturing the entire 3D geometry of the input structure; 3) inefficient predic... | Published as a conference paper at ICLR 2023 CONDITIONAL ANTIBODY DESIGN AS 3D EQUIVARI- ANT GRAPH TRANSLATION |
d257482844 | State-of-the-art computer vision models are mostly trained with supervised learning using human-labeled images, which limits their scalability due to the expensive annotation cost. While self-supervised representation learning has achieved impressive progress, it still requires a second stage of finetuning on labeled d... | Published as a conference paper at ICLR 2023 MASKED UNSUPERVISED SELF-TRAINING FOR LABEL- FREE IMAGE CLASSIFICATION |
d16926563 | We propose a framework for detecting action patterns from motion sequences and modeling the sensory-motor relationship of animals, using a generative recurrent neural network. The network has a discriminative part (classifying actions) and a generative part (predicting motion), whose recurrent cells are laterally conne... | Under review as a conference paper at ICLR 2017 LEARNING RECURRENT REPRESENTATIONS FOR HIERARCHICAL BEHAVIOR MODELING |
d211068987 | Autoencoder reconstructions are widely used for the task of unsupervised anomaly localization. Indeed, an autoencoder trained on normal data is expected to only be able to reconstruct normal features of the data, allowing the segmentation of anomalous pixels in an image via a simple comparison between the image and its... | ITERATIVE ENERGY-BASED PROJECTION ON A NOR- MAL DATA MANIFOLD FOR ANOMALY LOCALIZATION |
d237303776 | Exploration remains a central challenge for reinforcement learning (RL). Virtually all existing methods share the feature of a monolithic behaviour policy that changes only gradually (at best). In contrast, the exploratory behaviours of animals and humans exhibit a rich diversity, namely including forms of switching be... | Published as a conference paper at ICLR 2022 WHEN SHOULD AGENTS EXPLORE? |
d2239496 | A long-term goal of machine learning is to build intelligent conversational agents. One recent popular approach is to train end-to-end models on a large amount of real dialog transcripts between humans(Sordoni et al., 2015;Vinyals & Le, 2015;Shang et al., 2015). However, this approach leaves many questions unanswered a... | EVALUATING PREREQUISITE QUALITIES FOR LEARN- ING END-TO-END DIALOG SYSTEMS |
d235313383 | We propose the convergent graph solver (CGS) 1 , a deep learning method that learns iterative mappings to predict the properties of a graph system at its stationary state (fixed point) with guaranteed convergence. The forward propagation of CGS proceeds in three steps: (1) constructing the input-dependent linear contra... | Published as a conference paper at ICLR 2022 CONVERGENT GRAPH SOLVERS |
d231847140 | In novel class discovery (NCD), we are given labeled data from seen classes and unlabeled data from unseen classes, and we train clustering models for the unseen classes. However, the implicit assumptions behind NCD are still unclear. In this paper, we demystify assumptions behind NCD and find that high-level semantic ... | Published as a conference paper at ICLR 2022 META DISCOVERY: LEARNING TO DISCOVER NOVEL CLASSES GIVEN VERY LIMITED DATA |
d15659468 | Partition functions arise in a variety of settings, including conditional random fields, logistic regression, and latent gaussian models. In this paper, we consider semistochastic quadratic bound (SQB) methods for maximum likelihood estimation based on partition function optimization. Batch methods based on the quadrat... | Semistochastic quadratic bound methods |
d231800078 | Deep generative adversarial networks (GANs) have gained growing popularity in numerous scenarios, while usually suffer from high parameter complexities for resource-constrained real-world applications. However, the compression of GANs has less been explored. A few works show that heuristically applying compression tech... | Published as a conference paper at ICLR 2021 GANS CAN PLAY LOTTERY TICKETS TOO |
d214802067 | We formalize an equivalence between two popular methods for Bayesian inference: Stein variational gradient descent (SVGD) and black-box variational inference (BBVI). In particular, we show that BBVI corresponds precisely to SVGD when the kernel is the neural tangent kernel. Furthermore, we interpret SVGD and BBVI as ke... | The equivalence between Stein variational gradient descent and black-box variational inference |
d239016426 | Figure 1: One-shot domain adaptation: (left) a single reference image from domain B is used to refine a GAN G A to learn G B ; (center) every image in domain A has an analog in domain B that shares a latent code and many salient attributes; (right) because salient attributes are preserved in the new domain, many latent... | MIND THE GAP: DOMAIN GAP CONTROL FOR SINGLE SHOT DOMAIN ADAPTATION FOR GENERATIVE AD- VERSARIAL NETWORKS |
d248965094 | Calibration is defined as the ratio of the average predicted click rate to the true click rate. The optimization of calibration is essential to many online advertising recommendation systems because it directly affects the downstream bids in ads auctions and the amount of money charged to advertisers. Despite its impor... | Published as a conference paper at ICLR 2023 CALIBRATION MATTERS: TACKLING MAXIMIZATION BIAS IN LARGE-SCALE ADVERTISING RECOMMENDA- TION SYSTEMS |
d259298566 | Noisy labels can significantly affect the performance of deep neural networks (DNNs). In medical image segmentation tasks, annotations are error-prone due to the high demand in annotation time and in the annotators' expertise. Existing methods mostly assume noisy labels in different pixels are i.i.d. However, segmentat... | Published as a conference paper at ICLR 2023 LEARNING TO SEGMENT FROM NOISY ANNOTATIONS: A SPATIAL CORRECTION APPROACH |
d256459523 | We propose a method for learning topology-preserving data representations (dimensionality reduction). The method aims to provide topological similarity between the data manifold and its latent representation via enforcing the similarity in topological features (clusters, loops, 2D voids, etc.) and their localization. T... | LEARNING TOPOLOGY-PRESERVING DATA REPRESEN- TATIONS |
d209516262 | Sequential word order is important when processing text. Currently, neural networks (NNs) address this by modeling word position using position embeddings. The problem is that position embeddings capture the position of individual words, but not the ordered relationship (e.g., adjacency or precedence) between individua... | Published as a conference paper at ICLR 2020 ENCODING WORD ORDER IN COMPLEX EMBEDDINGS |
d257038893 | Although much of the success of Deep Learning builds on learning good representations, a rigorous method to evaluate their quality is lacking. In this paper, we treat the evaluation of representations as a model selection problem and propose to use the Minimum Description Length (MDL) principle to devise an evaluation ... | Published as a conference paper at ICLR 2023 EVALUATING REPRESENTATIONS WITH READOUT MODEL SWITCHING |
d12998557 | Most modern convolutional neural networks (CNNs) used for object recognition are built using the same principles: Alternating convolution and max-pooling layers followed by a small number of fully connected layers. We re-evaluate the state of the art for object recognition from small images with convolutional networks,... | Under review as a conference paper at ICLR 2015 STRIVING FOR SIMPLICITY: THE ALL CONVOLUTIONAL NET |
d252918484 | Deep Ensembles (DE) are a prominent approach for achieving excellent performance on key metrics such as accuracy, calibration, uncertainty estimation, and out-of-distribution detection. However, hardware limitations of real-world systems constrain to smaller ensembles and lower-capacity networks, significantly deterior... | PACKED-ENSEMBLES FOR EFFICIENT UNCERTAINTY ESTIMATION |
d247447572 | We present a general convergent class of reinforcement learning algorithms that is founded on two distinct principles: (1) mapping value estimates to a different space using arbitrary functions from a broad class, and (2) linearly decomposing the reward signal into multiple channels. The first principle enables incorpo... | Published as a conference paper at ICLR 2022 ORCHESTRATED VALUE MAPPING FOR REINFORCEMENT LEARNING |
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