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d247996741 | Neural network classifiers can largely rely on simple spurious features, such as backgrounds, to make predictions. However, even in these cases, we show that they still often learn core features associated with the desired attributes of the data, contrary to recent findings. Inspired by this insight, we demonstrate tha... | Published as a conference paper at ICLR 2023 LAST LAYER RE-TRAINING IS SUFFICIENT FOR ROBUSTNESS TO SPURIOUS CORRELATIONS |
d212768111 | This work seeks the possibility of generating the human face from voice solely based on the audio-visual data without any human-labeled annotations. To this end, we propose a multi-modal learning framework that links the inference stage and generation stage. First, the inference networks are trained to match the speake... | Published as a conference paper at ICLR 2020 FROM INFERENCE TO GENERATION: END-TO-END FULLY SELF-SUPERVISED GENERATION OF HUMAN FACE FROM SPEECH |
d252683631 | This paper builds bridges between two families of probabilistic algorithms: (hierarchical) variational inference (VI), which is typically used to model distributions over continuous spaces, and generative flow networks (GFlowNets), which have been used for distributions over discrete structures such as graphs. We demon... | Published as a conference paper at ICLR 2023 GFLOWNETS AND VARIATIONAL INFERENCE |
d16894878 | In this paper, we develop a framework for information theoretic learning based on infinitely divisible matrices. We formulate an entropy-like functional on positive definite matrices based on Renyi's axiomatic definition of entropy and examine some key properties of this functional that lead to the concept of infinite ... | Information Theoretic Learning with Infinitely Divisible Kernels |
d6138149 | Image super-resolution (SR) is an underdetermined inverse problem, where a large number of plausible high resolution images can explain the same downsampled image. Most current single image SR methods use empirical risk minimisation, often with a pixel-wise mean squared error (MSE) loss. However, the outputs from such ... | Published as a conference paper at ICLR 2017 AMORTISED MAP INFERENCE FOR IMAGE SUPER-RESOLUTION |
d219558972 | Designing an incentive compatible auction that maximizes expected revenue is a central problem in Auction Design. While theoretical approaches to the problem have hit some limits, a recent research direction initiated byDuetting et al. (2019)consists in building neural network architectures to find optimal auctions. We... | Published as a conference paper at ICLR 2021 AUCTION LEARNING AS A TWO-PLAYER GAME |
d235377213 | Evaluating the worst-case performance of a reinforcement learning (RL) agent under the strongest/optimal adversarial perturbations on state observations (within some constraints) is crucial for understanding the robustness of RL agents. However, finding the optimal adversary is challenging, in terms of both whether we ... | WHO IS THE STRONGEST ENEMY? TOWARDS OPTI- MAL AND EFFICIENT EVASION ATTACKS IN DEEP RL |
d235828753 | Modeling complex phenomena typically involves the use of both discrete and continuous variables. Such a setting applies across a wide range of problems, from identifying trends in time-series data to performing effective compositional scene understanding in images. Here, we propose Hybrid Memoised Wake-Sleep (HMWS), an... | HYBRID MEMOISED WAKE-SLEEP: APPROXIMATE IN- FERENCE AT THE DISCRETE-CONTINUOUS INTERFACE |
d247618647 | Most convergence guarantees for stochastic gradient descent with momentum (SGDm) rely on iid sampling. Yet, SGDm is often used outside this regime, in settings with temporally correlated input samples such as continual learning and reinforcement learning. Existing work has shown that SGDm with a decaying stepsize can c... | Published as a conference paper at ICLR 2022 RESONANCE IN WEIGHT SPACE: COVARIATE SHIFT CAN DRIVE DIVERGENCE OF SGD WITH MOMENTUM |
d1870512 | In this paper, we explore different ways to extend a recurrent neural network (RNN) to a deep RNN. We start by arguing that the concept of depth in an RNN is not as clear as it is in feedforward neural networks. By carefully analyzing and understanding the architecture of an RNN, however, we find three points of an RNN... | How to Construct Deep Recurrent Neural Networks |
d237592805 | The structural design of functional molecules, also called molecular optimization, is an essential chemical science and engineering task with important applications, such as drug discovery. Deep generative models and combinatorial optimization methods achieve initial success but still struggle with directly modeling di... | Differentiable Scaffolding Tree for Molecular Optimization |
d247158749 | Data augmentations are effective in improving the invariance of learning machines. We argue that the core challenge of data augmentations lies in designing data transformations that preserve labels. This is relatively straightforward for images, but much more challenging for graphs. In this work, we propose GraphAug, a... | Published as a conference paper at ICLR 2023 AUTOMATED DATA AUGMENTATIONS FOR GRAPH CLASSIFICATION |
d6949717 | We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within th... | Stochastic Pooling for Regularization of Deep Convolutional Neural Networks |
d214283684 | Research on exploration in reinforcement learning, as applied to Atari 2600 gameplaying, has emphasized tackling difficult exploration problems such as MON-TEZUMA'S REVENGE(Bellemare et al., 2016). Recently, bonus-based exploration methods, which explore by augmenting the environment reward, have reached above-human av... | ON BONUS-BASED EXPLORATION METHODS IN THE ARCADE LEARNING ENVIRONMENT |
d257206115 | Test-time adaptation (TTA) has shown to be effective at tackling distribution shifts between training and testing data by adapting a given model on test samples. However, the online model updating of TTA may be unstable and this is often a key obstacle preventing existing TTA methods from being deployed in the real wor... | Published as a conference paper at ICLR 2023 TOWARDS STABLE TEST-TIME ADAPTATION IN DYNAMIC WILD WORLD |
d249625698 | This work studies an algorithm, which we call magnetic mirror descent, that is inspired by mirror descent and the non-Euclidean proximal gradient algorithm.Our contribution is demonstrating the virtues of magnetic mirror descent as both an equilibrium solver and as an approach to reinforcement learning in two-player ze... | A UNIFIED APPROACH TO REINFORCEMENT LEARN-ING, QUANTAL RESPONSE EQUILIBRIA, AND TWO-PLAYER ZERO-SUM GAMES |
d232233726 | We propose a novel learning paradigm, Self-Imitation via Reduction (SIR), for solving compositional reinforcement learning problems. SIR is based on two core ideas: task reduction and self-imitation. Task reduction tackles a hard-to-solve task by actively reducing it to an easier task whose solution is known by the RL ... | Published as a conference paper at ICLR 2021 SOLVING COMPOSITIONAL REINFORCEMENT LEARN- ING PROBLEMS VIA TASK REDUCTION |
d247451199 | Synthesizing optimal controllers for dynamical systems often involves solving optimization problems with hard real-time constraints. These constraints determine the class of numerical methods that can be applied: computationally expensive but accurate numerical routines are replaced by fast and inaccurate methods, trad... | Published as a conference paper at ICLR 2022 NEURAL SOLVERS FOR FAST AND ACCURATE NUMER- ICAL OPTIMAL CONTROL |
d246634966 | We introduce Synthetic Environments (SEs) and Reward Networks (RNs), represented by neural networks, as proxy environment models for training Reinforcement Learning (RL) agents. We show that an agent, after being trained exclusively on the SE, is able to solve the corresponding real environment. While an SE acts as a f... | Published as a conference paper at ICLR 2022 LEARNING SYNTHETIC ENVIRONMENTS AND RE- WARD NETWORKS FOR REINFORCEMENT LEARNING |
d214693392 | Recently, there has been a lot of interest in using neural networks for solving partial differential equations. A number of neural network-based partial differential equation solvers have been formulated which provide performances equivalent, and in some cases even superior, to classical solvers. However, these neural ... | Published at the DeepDiffEq workshop under ICLR 2020 LEARNING TO SOLVE DIFFERENTIAL EQUATIONS ACROSS INITIAL CONDITIONS |
d222067132 | We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors such as sparsity or low-rankness. To approximate softmax attentionkern... | Published as a conference paper at ICLR 2021 RETHINKING ATTENTION WITH PERFORMERS |
d1926319 | Several recent publications have proposed methods for mapping images into continuous semantic embedding spaces. In some cases the embedding space is trained jointly with the image transformation. In other cases the semantic embedding space is established by an independent natural language processing task, and then the ... | Zero-Shot Learning by Convex Combination of Semantic Embeddings |
d210164926 | Adversarial training, a method for learning robust deep networks, is typically assumed to be more expensive than traditional training due to the necessity of constructing adversarial examples via a first-order method like projected gradient decent (PGD). In this paper, we make the surprising discovery that it is possib... | Published as a conference paper at ICLR 2020 FAST IS BETTER THAN FREE: REVISITING ADVERSARIAL TRAINING |
d249191720 | Existing dialogue modeling methods have achieved promising performance on various dialogue tasks with the aid of Transformer and the large-scale pre-trained language models. However, some recent studies revealed that the context representations produced by these methods suffer the problem of anisotropy. In this paper, ... | Published as a conference paper at ICLR 2023 LEARNING LOCALITY AND ISOTROPY IN DIALOGUE MODELING |
d2918187 | In this paper we introduce a new unsupervised reinforcement learning method for discovering the set of intrinsic options available to an agent. This set is learned by maximizing the number of different states an agent can reliably reach, as measured by the mutual information between the set of options and option termin... | VARIATIONAL INTRINSIC CONTROL |
d231718721 | Contrasting the previous evidence that neurons in the later layers of a Convolutional Neural Network (CNN) respond to complex object shapes, recent studies have shown that CNNs actually exhibit a 'texture bias': given an image with both texture and shape cues (e.g., a stylized image), a CNN is biased towards predicting... | Published as a conference paper at ICLR 2021 SHAPE OR TEXTURE: UNDERSTANDING DISCRIMINATIVE FEATURES IN CNNS |
d231749857 | Controllable semantic image editing enables a user to change entire image attributes with few clicks, e.g., gradually making a summer scene look like it was taken in winter. Classic approaches for this task use a Generative Adversarial Net (GAN) to learn a latent space and suitable latent-space transformations. However... | Published as a conference paper at ICLR 2021 ENJOY YOUR EDITING: CONTROLLABLE GANS FOR IMAGE EDITING VIA LATENT SPACE NAVIGATION |
d244270768 | Being able to predict the mental states of others is a key factor to effective social interaction. It is also crucial for distributed multi-agent systems, where agents are required to communicate and cooperate. In this paper, we introduce such an important social-cognitive skill, i.e. Theory of Mind (ToM), to build soc... | Published as a conference paper at ICLR 2022 TOM2C: TARGET-ORIENTED MULTI-AGENT COMMU- NICATION AND COOPERATION WITH THEORY OF MIND |
d252846263 | Stochastic gradient descent-ascent (SGDA) is one of the main workhorses for solving finite-sum minimax optimization problems. Most practical implementations of SGDA randomly reshuffle components and sequentially use them (i.e., without-replacement sampling); however, there are few theoretical results on this approach f... | Published as a conference paper at ICLR 2023 SGDA WITH SHUFFLING: FASTER CONVERGENCE FOR NONCONVEX-PŁ MINIMAX OPTIMIZATION |
d257255342 | Many existing group fairness-aware training methods aim to achieve the group fairness by either re-weighting underrepresented groups based on certain rules or using weakly approximated surrogates for the fairness metrics in the objective as regularization terms. Although each of the learning schemes has its own strengt... | RE-WEIGHTING BASED GROUP FAIRNESS REGULAR- IZATION VIA CLASSWISE ROBUST OPTIMIZATION |
d252668838 | Figure 1: Image translation results by DiffuseIT. Our model can generate high-quality translation outputs using both text and image conditions. More results can be found in the experiment section.ABSTRACTDiffusion-based image translation guided by semantic texts or a single target image has enabled flexible style trans... | DIFFUSION-BASED IMAGE TRANSLATION USING DIS- ENTANGLED STYLE AND CONTENT REPRESENTATION |
d256615188 | Magnetic resonance imaging (MRI) is a common and life-saving medical imaging technique. However, acquiring high signal-to-noise ratio MRI scans requires long scan times, resulting in increased costs and patient discomfort, and decreased throughput. Thus, there is great interest in denoising MRI scans, especially for th... | Published as a conference paper at ICLR 2023 DDM 2 : SELF-SUPERVISED DIFFUSION MRI DENOIS- ING WITH GENERATIVE DIFFUSION MODELS |
d232185104 | Pretrained text encoders, such as BERT, have been applied increasingly in various natural language processing (NLP) tasks, and have recently demonstrated significant performance gains. However, recent studies have demonstrated the existence of social bias in these pretrained NLP models. Although prior works have made p... | Published as a conference paper at ICLR 2021 FAIRFIL: CONTRASTIVE NEURAL DEBIASING METHOD FOR PRETRAINED TEXT ENCODERS |
d246442150 | In this paper, we question the rationale behind propagating large numbers of parameters through a distributed system during federated learning. We start by examining the rank characteristics of the subspace spanned by gradients across epochs (i.e., the gradient-space) in centralized model training, and observe that thi... | Published as a conference paper at ICLR 2022 RECYCLING MODEL UPDATES IN FEDERATED LEARNING: ARE GRADIENT SUBSPACES LOW-RANK? |
d233740397 | We present an unsupervised approach that converts the input speech of any individual into audiovisual streams of potentially-infinitely many output speakers. Our approach builds on simple autoencoders that project out-of-sample data onto the distribution of the training set. We use Exemplar Autoencoders to learn the vo... | Published as a conference paper at ICLR 2021 UNSUPERVISED AUDIOVISUAL SYNTHESIS VIA EXEMPLAR AUTOENCODERS |
d257232596 | Neighbor embedding methods t-SNE and UMAP are the de facto standard for visualizing high-dimensional datasets. Motivated from entirely different viewpoints, their loss functions appear to be unrelated. In practice, they yield strongly differing embeddings and can suggest conflicting interpretations of the same data. Th... | Published as a conference paper at ICLR 2023 FROM t-SNE TO UMAP WITH CONTRASTIVE LEARNING |
d235436105 | Multi-hop logical reasoning is an established problem in the field of representation learning on knowledge graphs (KGs). It subsumes both one-hop link prediction as well as other more complex types of logical queries. However, existing algorithms operate only on classical, triple-based graphs, whereas modern KGs often ... | Published as a conference paper at ICLR 2022 QUERY EMBEDDING ON HYPER-RELATIONAL KNOWLEDGE GRAPHS |
d246431097 | The growing public concerns on data privacy in face recognition can be greatly addressed by the federated learning (FL) paradigm. However, conventional FL methods perform poorly due to the uniqueness of the task: broadcasting class centers among clients is crucial for recognition performances but leads to privacy leaka... | IMPROVING FEDERATED LEARNING FACE RECOGNI- TION VIA PRIVACY-AGNOSTIC CLUSTERS |
d244714983 | We study a class of algorithms for solving bilevel optimization problems in both stochastic and deterministic settings when the inner-level objective is strongly convex. Specifically, we consider algorithms based on inexact implicit differentiation and we exploit a warm-start strategy to amortize the estimation of the ... | Published as a conference paper at ICLR 2022 AMORTIZED IMPLICIT DIFFERENTIATION FOR STOCHASTIC BILEVEL OPTIMIZATION |
d3431470 | We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not. By assimilating generic message-passing inference algorithms with their neural-network counterparts, we defin... | FEW-SHOT LEARNING WITH GRAPH NEURAL NET- WORKS |
d46932612 | Our understanding of reinforcement learning (RL) has been shaped by theoretical and empirical results that were obtained decades ago using tabular representations and linear function approximators. These results suggest that RL methods that use temporal differencing (TD) are superior to direct Monte Carlo estimation (M... | Published as a conference paper at ICLR 2018 TD OR NOT TD: ANALYZING THE ROLE OF TEMPORAL DIFFERENCING IN DEEP REINFORCEMENT LEARNING |
d5959482 | We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. ... | Efficient Estimation of Word Representations in Vector Space |
d251719318 | Representation learning often plays a critical role in avoiding the curse of dimensionality in reinforcement learning. A representative class of algorithms exploits spectral decomposition of the stochastic transition dynamics to construct representations that enjoy strong theoretical properties in idealized settings. H... | Published as a conference paper at ICLR 2023 SPECTRAL DECOMPOSITION REPRESENTATION FOR REINFORCEMENT LEARNING |
d1662233 | Representation learning systems typically rely on massive amounts of labeled data in order to be trained to high accuracy. Recently, high-dimensional parametric models like neural networks have succeeded in building rich representations using either compressive, reconstructive or supervised criteria. However, the seman... | BAYESIAN REPRESENTATION LEARNING WITH ORACLE CONSTRAINTS |
d211096885 | While generative adversarial networks (GAN) have been widely adopted in various topics, in this paper we generalize the standard GAN to a new perspective by treating realness as a random variable that can be estimated from multiple angles. In this generalized framework, referred to as RealnessGAN 1 , the discriminator ... | Published as a conference paper at ICLR 2020 REAL OR NOT REAL, THAT IS THE QUESTION |
d203594002 | Learning to align multiple datasets is an important problem with many applications, and it is especially useful when we need to integrate multiple experiments or correct for confounding. Optimal transport (OT) is a principled approach to align datasets, but a key challenge in applying OT is that we need to specify a tr... | Published as a conference paper at ICLR 2020 LEARNING TRANSPORT COST FROM SUBSET CORRE- SPONDENCE |
d16734174 | We develop a representation suitable for the unconstrained recognition of words in natural images, where unconstrained means that there is no fixed lexicon and words have unknown length.To this end we propose a convolutional neural network (CNN) based architecture which incorporates a Conditional Random Field (CRF) gra... | DEEP STRUCTURED OUTPUT LEARNING FOR UNCONSTRAINED TEXT RECOGNITION |
d49745096 | Disjoint Mapping Network for Cross-modal Matching of Voices and Faces Contrastive Loss Triplet Loss Multi-task Classification Multiple Covariates Supervision | |
d225062556 | The label shift problem refers to the supervised learning setting where the train and test label distributions do not match. Existing work addressing label shift usually assumes access to an unlabelled test sample. This sample may be used to estimate the test label distribution, and to then train a suitably re-weighted... | Published as a conference paper at ICLR 2021 COPING WITH LABEL SHIFT VIA DISTRIBUTIONALLY ROBUST OPTIMISATION |
d88522390 | This paper presents a novel two-step approach for the fundamental problem of learning an optimal map from one distribution to another. First, we learn an optimal transport (OT) plan, which can be thought as a one-to-many map between the two distributions. To that end, we propose a stochastic dual approach of regularize... | LARGE-SCALE OPTIMAL TRANSPORT AND MAPPING ESTIMATION |
d213695995 | Recommendation is a prevalent application of machine learning that affects many users; therefore, it is important for recommender models to be accurate and interpretable. In this work, we propose a method to both interpret and augment the predictions of black-box recommender systems. In particular, we propose to interp... | Published as a conference paper at ICLR 2020 FEATURE INTERACTION INTERPRETABILITY: A CASE FOR EXPLAINING AD-RECOMMENDATION SYSTEMS VIA NEURAL INTERACTION DETECTION |
d256389347 | Federated learning is a distributed paradigm that allows multiple parties to collaboratively train deep models without exchanging the raw data. However, the data distribution among clients is naturally non-i.i.d., which leads to severe degradation of the learnt model. The primary goal of this paper is to develop a robu... | Published as a conference paper at ICLR 2023 FEDFA: FEDERATED FEATURE AUGMENTATION |
d14089312 | The success of CNNs in various applications is accompanied by a significant increase in the computation and parameter storage costs. Recent efforts toward reducing these overheads involve pruning and compressing the weights of various layers without hurting original accuracy. However, magnitude-based pruning of weights... | Published as a conference paper at ICLR 2017 PRUNING FILTERS FOR EFFICIENT CONVNETS |
d61857474 | We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP is a generalization of the nested Chinese restaurant process (nCRP) that allows each word to follow its own path to a topic node according to a document-specific distribution on a shared tree. This alleviates the rigid, ... | A Nested HDP for Hierarchical Topic Models |
d257233135 | Self-supervised learning has significantly improved the performance of many NLP tasks. However, how can self-supervised learning discover useful representations, and why is it better than traditional approaches such as probabilistic models are still largely unknown. In this paper, we focus on the context of topic model... | UNDERSTANDING THE ROBUSTNESS OF SELF- SUPERVISED LEARNING THROUGH TOPIC MODELING |
d195346786 | Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL. On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori. However, in complex environments where transition models need t... | TREEQN AND ATREEC: DIFFERENTIABLE TREE PLANNING FOR DEEP REINFORCEMENT LEARNING |
d238856707 | Many-to-one maps are ubiquitous in machine learning, from the image recognition model that assigns a multitude of distinct images to the concept of "cat" to the time series forecasting model which assigns a range of distinct time-series to a single scalar regression value. While the primary use of such models is natura... | Published as a conference paper at ICLR 2022 BUNDLE NETWORKS: FIBER BUNDLES, LOCAL TRIV- IALIZATIONS, AND A GENERATIVE APPROACH TO EX- PLORING MANY-TO-ONE MAPS |
d15389304 | Convolutional neural networks perform well on object recognition because of a number of recent advances: rectified linear units (ReLUs), data augmentation, dropout, and large labelled datasets. Unsupervised data has been proposed as another way to improve performance. Unfortunately, unsupervised pre-training is not use... | AN ANALYSIS OF UNSUPERVISED PRE-TRAINING IN LIGHT OF RECENT ADVANCES |
d3555598 | Computing universal distributed representations of sentences is a fundamental task in natural language processing. We propose a method to learn such representations by encoding the suffixes of word sequences in a sentence and training on the Stanford Natural Language Inference (SNLI) dataset. We demonstrate the effecti... | Workshop track -ICLR 2018 SUFISENT -UNIVERSAL SENTENCE REPRESENTA- TIONS USING SUFFIX ENCODINGS |
d249847976 | Modern deep learning systems are increasingly deployed in situations such as personalization and federated learning where it is necessary to support i) learning on small amounts of data, and ii) communication efficient distributed training protocols. In this work, we develop FiLM Transfer (FIT) which fulfills these req... | Published as a conference paper at ICLR 2023 FIT: PARAMETER EFFICIENT FEW-SHOT TRANSFER LEARNING FOR PERSONALIZED AND FEDERATED IMAGE CLASSIFICATION |
d174802916 | Due to the statistical complexity of video, the high degree of inherent stochasticity, and the sheer amount of data, generating natural video remains a challenging task. State-of-the-art video generation models often attempt to address these issues by combining sometimes complex, usually video-specific neural network a... | Published as a conference paper at ICLR 2020 SCALING AUTOREGRESSIVE VIDEO MODELS |
d31598573 | Learning policies for complex tasks that require multiple different skills is a major challenge in reinforcement learning (RL). It is also a requirement for its deployment in real-world scenarios. This paper proposes a novel framework for efficient multi-task reinforcement learning. Our framework trains agents to emplo... | Under review as a conference paper at ICLR 2018 HIERARCHICAL AND INTERPRETABLE SKILL ACQUI- SITION IN MULTI-TASK REINFORCEMENT LEARNING |
d231798234 | Robustness against word substitutions has a well-defined and widely acceptable form, i.e., using semantically similar words as substitutions, and thus it is considered as a fundamental stepping-stone towards broader robustness in natural language processing. Previous defense methods capture word substitutions in vector... | Published as a conference paper at ICLR 2021 TOWARDS ROBUSTNESS AGAINST NATURAL LANGUAGE WORD SUBSTITUTIONS |
d257038997 | Knowledge distillation has been shown to be a powerful model compression approach to facilitate the deployment of pre-trained language models in practice. This paper focuses on task-agnostic distillation. It produces a compact pre-trained model that can be easily fine-tuned on various tasks with small computational cos... | HOMODISTIL: HOMOTOPIC TASK-AGNOSTIC DISTIL- LATION OF PRE-TRAINED TRANSFORMERS |
d231719419 | Hopfield networks (HNs) and Restricted Boltzmann Machines (RBMs) are two important models at the interface of statistical physics, machine learning, and neuroscience. Recently, there has been interest in the relationship between HNs and RBMs, due to their similarity under the statistical mechanics formalism. An exact m... | Published as a conference paper at ICLR 2021 ON THE MAPPING BETWEEN HOPFIELD NETWORKS AND RESTRICTED BOLTZMANN MACHINES |
d257232672 | We create a reusable Transformer, BrainBERT, for intracranial field potential recordings bringing modern representation learning approaches to neuroscience. Much like in NLP and speech recognition, this Transformer enables classifying complex concepts, i.e., decoding neural data, with higher accuracy and with much less... | Published as a conference paper at ICLR 2023 BRAINBERT: SELF-SUPERVISED REPRESENTATION LEARNING FOR INTRACRANIAL RECORDINGS |
d204806091 | Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structure of proteins and other macromolecular complexes at near-atomic resolution. In single particle cryo-EM, the central problem is to reconstruct the 3D structure of a macromolecule from 10 4−7 noisy and randomly oriented 2D projection i... | Published as a conference paper at ICLR 2020 RECONSTRUCTING CONTINUOUS DISTRIBUTIONS OF 3D PROTEIN STRUCTURE FROM CRYO-EM IMAGES |
d222125159 | Replica exchange stochastic gradient Langevin dynamics (reSGLD) has shown promise in accelerating the convergence in non-convex learning; however, an excessively large correction for avoiding biases from noisy energy estimators has limited the potential of the acceleration. To address this issue, we study the variance ... | ACCELERATING CONVERGENCE OF REPLICA EX- CHANGE STOCHASTIC GRADIENT MCMC VIA VARI- ANCE REDUCTION |
d257496462 | Oversmoothing is a common phenomenon in a wide range of Graph Neural Networks (GNNs) and Transformers, where performance worsens as the number of layers increases. Instead of characterizing oversmoothing from the view of complete collapse in which representations converge to a single point, we dive into a more general ... | CONTRANORM: A CONTRASTIVE LEARNING PER- SPECTIVE ON OVERSMOOTHING AND BEYOND |
d246705922 | Neural network quantization is a promising compression technique to reduce memory footprint and save energy consumption, potentially leading to real-time inference. However, there is a performance gap between quantized and fullprecision models. To reduce it, existing quantization approaches require highprecision INT32 ... | Published as a conference paper at ICLR 2022 F8NET: FIXED-POINT 8-BIT ONLY MULTIPLICATION FOR NETWORK QUANTIZATION |
d245005831 | Numerous applications of machine learning involve representing probability distributions over high-dimensional data. We propose autoregressive quantile flows, a flexible class of normalizing flow models trained using a novel objective based on proper scoring rules. Our objective does not require calculating computation... | Published as a conference paper at ICLR 2022 AUTOREGRESSIVE QUANTILE FLOWS FOR PREDICTIVE UNCERTAINTY ESTIMATION |
d247958122 | The paradigm of worst-group loss minimization has shown its promise in avoiding to learn spurious correlations, but requires costly additional supervision on spurious attributes. To resolve this, recent works focus on developing weaker forms of supervision-e.g., hyperparameters discovered with a small number of group-l... | SPREAD SPURIOUS ATTRIBUTE: IMPROVING WORST-GROUP ACCURACY WITH SPURI- OUS ATTRIBUTE ESTIMATION |
d226048947 | Partial differential equations (PDEs) are used to describe a variety of physical phenomena.Often these equations do not have analytical solutions and numerical approximations are used instead.One of the common methods to solve PDEs is the finite element method.Computing derivative information of the solution with respe... | |
d204906218 | In this work, we propose a new method to integrate two recent lines of work: unsupervised induction of shallow semantics (e.g., semantic roles) and factorization of relations in text and knowledge bases.Our model consists of two components:(1) an encoding component: a semantic role labeling model which predicts roles g... | Under review as a workshop contribution at ICLR 2015 INDUCING SEMANTIC REPRESENTATION FROM TEXT BY JOINTLY PREDICTING AND FACTORIZING RELA-TIONS |
d231592851 | Predictive uncertainty estimation is an essential next step for the reliable deployment of deep object detectors in safety-critical tasks. In this work, we focus on estimating predictive distributions for bounding box regression output with variance networks. We show that in the context of object detection, training va... | Published as a conference paper at ICLR 2021 ESTIMATING AND EVALUATING REGRESSION PREDIC- TIVE UNCERTAINTY IN DEEP OBJECT DETECTORS |
d213987212 | Mode connectivity provides novel geometric insights on analyzing loss landscapes and enables building high-accuracy pathways between well-trained neural networks. In this work, we propose to employ mode connectivity in loss landscapes to study the adversarial robustness of deep neural networks, and provide novel method... | Published as a conference paper at ICLR 2020 BRIDGING MODE CONNECTIVITY IN LOSS LANDSCAPES AND ADVERSARIAL ROBUSTNESS |
d22014305 | Multi-task learning (MTL) with neural networks leverages commonalities in tasks to improve performance, but often suffers from task interference which reduces the benefits of transfer. To address this issue we introduce the routing network paradigm, a novel neural network and training algorithm. A routing network is a ... | ROUTING NETWORKS: ADAPTIVE SELECTION OF NON-LINEAR FUNCTIONS FOR MULTI-TASK LEARN- ING |
d221083147 | Although widely adopted, existing approaches for fine-tuning pre-trained language models have been shown to be unstable across hyper-parameter settings, motivating recent work on trust region methods. In this paper, we present a simplified and efficient method rooted in trust region theory that replaces previously used... | BETTER FINE-TUNING BY REDUCING REPRESENTA- TIONAL COLLAPSE |
d245117574 | Building embodied intelligent agents that can interact with 3D indoor environments has received increasing research attention in recent years. While most works focus on single-object or agent-object visual functionality and affordances, our work proposes to study a new kind of visual relationship that is also important... | IFR-EXPLORE: LEARNING INTER-OBJECT FUNC- TIONAL RELATIONSHIPS IN 3D INDOOR SCENES |
d239998683 | k-means clustering is a well-studied problem due to its wide applicability. Unfortunately, there exist strong theoretical limits on the performance of any algorithm for the k-means problem on worst-case inputs. To overcome this barrier, we consider a scenario where "advice" is provided to help perform clustering. Speci... | Learning-Augmented k-means Clustering |
d54438210 | Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures.However, the prohibitive computational demand of conventional NAS algorithms (e.g. 10 4 GPU hours) makes it difficult to directly search the architectures on large-scale tasks (e.g.ImageNet).Differentia... | |
d233025422 | Many real-world applications such as robotics provide hard constraints on power and compute that limit the viable model complexity of Reinforcement Learning (RL) agents. Similarly, in many distributed RL settings, acting is done on unaccelerated hardware such as CPUs, which likewise restricts model size to prevent intr... | Published as a conference paper at ICLR 2021 EFFICIENT TRANSFORMERS IN REINFORCEMENT LEARNING USING ACTOR-LEARNER DISTILLATION |
d247315339 | In the online continual learning paradigm, agents must learn from a changing distribution while respecting memory and compute constraints. Experience Replay (ER), where a small subset of past data is stored and replayed alongside new data, has emerged as a simple and effective learning strategy. In this work, we focus ... | Published as a conference paper at ICLR 2022 NEW INSIGHTS ON REDUCING ABRUPT REPRESENTA- TION CHANGE IN ONLINE CONTINUAL LEARNING |
d252846076 | MuZero Unplugged presents a promising approach for offline policy learning from logged data. It conducts Monte-Carlo Tree Search (MCTS) with a learned model and leverages Reanalyze algorithm to learn purely from offline data. For good performance, MCTS requires accurate learned models and a large number of simulations,... | Published as a conference paper at ICLR 2023 EFFICIENT OFFLINE POLICY OPTIMIZATION WITH A LEARNED MODEL |
d253098754 | Weather and climate simulations produce petabytes of high-resolution data that are later analyzed by researchers in order to understand climate change or severe weather. We propose a new method of compressing this multidimensional weather and climate data: a coordinate-based neural network is trained to overfit the dat... | Published as a conference paper at ICLR 2023 COMPRESSING MULTIDIMENSIONAL WEATHER AND CLIMATE DATA INTO NEURAL NETWORKS |
d210839718 | Batch Normalization (BN) is one of the most widely used techniques in Deep Learning field. But its performance can awfully degrade with insufficient batch size. This weakness limits the usage of BN on many computer vision tasks like detection or segmentation, where batch size is usually small due to the constraint of m... | TOWARDS STABILIZING BATCH STATISTICS IN BACK- WARD PROPAGATION OF BATCH NORMALIZATION |
d253098208 | Sampling from a target measure whose density is only known up to a normalization constant is a fundamental problem in computational statistics and machine learning. In this paper, we present a new optimization-based method for sampling called mollified interaction energy descent (MIED). MIED minimizes a new class of en... | Published as a conference paper at ICLR 2023 SAMPLING WITH MOLLIFIED INTERACTION ENERGY DESCENT |
d257804924 | Image steganography is the process of concealing secret information in images through imperceptible changes. Recent work has formulated this task as a classic constrained optimization problem. In this paper, we argue that image steganography is inherently performed on the (elusive) manifold of natural images, and propo... | Published as a conference paper at ICLR 2023 LEARNING ITERATIVE NEURAL OPTIMIZERS FOR IM- AGE STEGANOGRAPHY |
d252595921 | Mathematical reasoning, a core ability of human intelligence, presents unique challenges for machines in abstract thinking and logical reasoning. Recent large pre-trained language models such as GPT-3 have achieved remarkable progress on mathematical reasoning tasks written in text form, such as math word problems (MWP... | Published as a conference paper at ICLR 2023 DYNAMIC PROMPT LEARNING VIA POLICY GRADIENT FOR SEMI-STRUCTURED MATHEMATICAL REASONING |
d257378217 | Recent Self-Supervised Learning (SSL) methods are able to learn feature representations that are invariant to different data augmentations, which can then be transferred to downstream tasks of interest. However, different downstream tasks require different invariances for their best performance, so the optimal choice o... | MAST: MASKED AUGMENTATION SUBSPACE TRAIN- ING FOR GENERALIZABLE SELF-SUPERVISED PRIORS |
d249889641 | In this paper we show how to achieve state-of-the-art certified adversarial robustness to 2 -norm bounded perturbations by relying exclusively on off-the-shelf pretrained models. To do so, we instantiate the denoised smoothing approach of Salman et al. (2020) by combining a pretrained denoising diffusion probabilistic ... | Published as a conference paper at ICLR 2023 |
d252683970 | A large class of hyperbolic and advection-dominated PDEs can have solutions with discontinuities. This paper investigates, both theoretically and empirically, the operator learning of PDEs with discontinuous solutions. We rigorously prove, in terms of lower approximation bounds, that methods which entail a linear recon... | NONLINEAR RECONSTRUCTION FOR OPERATOR LEARNING OF PDES WITH DISCONTINUITIES A PREPRINT |
d211126472 | Time series forecasting is often fundamental to scientific and engineering problems and enables decision making. With ever increasing data set sizes, a trivial solution to scale up predictions is to assume independence between interacting time series. However, modeling statistical dependencies can improve accuracy and ... | Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows |
d247450683 | State-of-the-art maximum entropy models for texture synthesis are built from statistics relying on image representations defined by convolutional neural networks (CNN). Such representations capture rich structures in texture images, outperforming wavelet-based representations in this regard. However, conversely to neur... | Published as a conference paper at ICLR 2022 GENERALIZED RECTIFIER WAVELET COVARIANCE MODELS FOR TEXTURE SYNTHESIS |
d165163737 | Training large deep neural networks on massive datasets is computationally very challenging. There has been recent surge in interest in using large batch stochastic optimization methods to tackle this issue. The most prominent algorithm in this line of research is LARS, which by employing layerwise adaptive learning ra... | Published as a conference paper at ICLR 2020 LARGE BATCH OPTIMIZATION FOR DEEP LEARNING: TRAINING BERT IN 76 MINUTES |
d245144350 | A wide variety of deep generative models has been developed in the past decade. Yet, these models often struggle with simultaneously addressing three key requirements including: high sample quality, mode coverage, and fast sampling. We call the challenge imposed by these requirements the generative learning trilemma, a... | Published as a conference paper at ICLR 2022 TACKLING THE GENERATIVE LEARNING TRILEMMA WITH DENOISING DIFFUSION GANS |
d9665723 | Convolutional Neural Networks spread through computer vision like a wildfire, impacting almost all visual tasks imaginable. Despite this, few researchers dare to train their models from scratch. Most work builds on one of a handful of Im-ageNet pre-trained models, and fine-tunes or adapts these for specific tasks. This... | DATA-DEPENDENT INITIALIZATIONS OF CONVOLUTIONAL NEURAL NETWORKS |
d52897771 | Most of the prior work on multi-agent reinforcement learning (MARL) achieves optimal collaboration by directly controlling the agents to maximize a common reward. In this paper, we aim to address this from a different angle. In particular, we consider scenarios where there are self-interested agents (i.e., worker agent... | M 3 RL: MIND-AWARE MULTI-AGENT MANAGEMENT REINFORCEMENT LEARNING |
d202750335 | It has been widely recognized that adversarial examples can be easily crafted to fool deep networks, which mainly root from the locally unreasonable behavior nearby input examples. Applying mixup in training provides an effective mechanism to improve generalization performance and model robustness against adversarial p... | Published as a conference paper at ICLR 2020 MIXUP INFERENCE: BETTER EXPLOITING MIXUP TO DEFEND ADVERSARIAL ATTACKS |
d67856088 | A major goal of unsupervised learning is to discover data representations that are useful for subsequent tasks, without access to supervised labels during training. Typically, this involves minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations us... | META-LEARNING UPDATE RULES FOR UNSUPER- VISED REPRESENTATION LEARNING |
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