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d2208884 | We develop a new method for visualizing and refining the invariances of learned representations. Specifically, we test for a general form of invariance, linearization, in which the action of a transformation is confined to a low-dimensional subspace. Given two reference images (typically, differing by some transformati... | GEODESICS OF LEARNED REPRESENTATIONS |
d209439434 | It is well-known that classifiers are vulnerable to adversarial perturbations. To defend against adversarial perturbations, various certified robustness results have been derived. However, existing certified robustnesses are limited to top-1 predictions. In many real-world applications, top-k predictions are more relev... | Published as a conference paper at ICLR 2020 CERTIFIED ROBUSTNESS FOR TOP-k PREDICTIONS AGAINST ADVERSARIAL PERTURBATIONS VIA RAN- DOMIZED SMOOTHING |
d219708602 | We introduce the Generalized Energy Based Model (GEBM) for generative modelling. These models combine two trained components: a base distribution (generally an implicit model), which can learn the support of data with low intrinsic dimension in a high dimensional space; and an energy function, to refine the probability... | Published as a conference paper at ICLR 2021 GENERALIZED ENERGY BASED MODELS |
d9482504 | Unsupervised feature learning has shown impressive results for a wide range of input modalities, in particular for object classification tasks in computer vision. Using a large amount of unlabeled data, unsupervised feature learning methods are utilized to construct high-level representations that are discriminative en... | Unsupervised Feature Learning for low-level Local Image Descriptors |
d250607914 | Randomized smoothing is sound when using infinite precision. However, we show that randomized smoothing is no longer sound for limited floating-point precision. We present a simple example where randomized smoothing certifies a radius of 1.26 around a point, even though there is an adversarial example in the distance 0... | SOUND RANDOMIZED SMOOTHING IN FLOATING- POINT ARITHMETIC |
d252668796 | Deep neural networks have shown excellent prospects in speech separation tasks. However, obtaining good results while keeping a low model complexity remains challenging in real-world applications. In this paper, we provide a bio-inspired efficient encoder-decoder architecture by mimicking the brain's top-down attention... | Published as a conference paper at ICLR 2023 AN EFFICIENT ENCODER-DECODER ARCHITECTURE WITH TOP-DOWN ATTENTION FOR SPEECH SEPARA- TION |
d250279951 | It is essential yet challenging for future home-assistant robots to understand and manipulate diverse 3D objects in daily human environments. Towards building scalable systems that can perform diverse manipulation tasks over various 3D shapes, recent works have advocated and demonstrated promising results learning visu... | Published as a conference paper at ICLR 2023 DUALAFFORD: LEARNING COLLABORATIVE VISUAL AFFORDANCE FOR DUAL-GRIPPER MANIPULATION |
d222133374 | Federated Learning (FL) is a method of training machine learning models on private data distributed over a large number of possibly heterogeneous clients such as mobile phones and IoT devices. In this work, we propose a new federated learning framework named HeteroFL to address heterogeneous clients equipped with very ... | HETEROFL: COMPUTATION AND COMMUNICATION EFFICIENT FEDERATED LEARNING FOR HETEROGE- NEOUS CLIENTS |
d235795569 | Many reinforcement learning (RL) agents require a large amount of experience to solve tasks. We propose Contrastive BERT for RL (COBERL), an agent that combines a new contrastive loss and a hybrid LSTM-transformer architecture to tackle the challenge of improving data efficiency. COBERL enables efficient and robust lea... | COBERL: CONTRASTIVE BERT FOR REINFORCE- MENT LEARNING |
d204806122 | To deflect adversarial attacks, a range of "certified" classifiers have been proposed. In addition to labeling an image, certified classifiers produce (when possible) a certificate guaranteeing that the input image is not an p -bounded adversarial example. We present a new attack that exploits not only the labelling fu... | BREAKING CERTIFIED DEFENSES: SEMANTIC ADVER- SARIAL EXAMPLES WITH SPOOFED ROBUSTNESS CER- TIFICATES |
d252873240 | Oriented object detection emerges in many applications from aerial images to autonomous driving, while many existing detection benchmarks are annotated with horizontal bounding box only which is also less costive than fine-grained rotated box, leading to a gap between the readily available training corpus and the risin... | Published as a conference paper at ICLR 2023 H2RBOX: HORIZONTAL BOX ANNOTATION IS ALL YOU NEED FOR ORIENTED OBJECT DETECTION |
d253707860 | Ensembling has proven to be a powerful technique for boosting model performance, uncertainty estimation, and robustness in supervised learning. Advances in self-supervised learning (SSL) enable leveraging large unlabeled corpora for state-of-the-art few-shot and supervised learning performance. In this paper, we explor... | Published as a conference paper at ICLR 2023 WEIGHTED ENSEMBLE SELF-SUPERVISED LEARNING |
d251563977 | Linear regression is a fundamental tool for statistical analysis. This has motivated the development of linear regression methods that also satisfy differential privacy and thus guarantee that the learned model reveals little about any one data point used to construct it. However, existing differentially private soluti... | Published as a conference paper at ICLR 2023 EASY DIFFERENTIALLY PRIVATE LINEAR REGRESSION |
d235458091 | Multimodal contrastive learning methods like CLIP train on noisy and uncurated training datasets. This is cheaper than labeling datasets manually, and even improves out-of-distribution robustness. We show that this practice makes backdoor and poisoning attacks a significant threat. By poisoning just 0.01% of a dataset ... | Published as a conference paper at ICLR 2022 POISONING AND BACKDOORING CONTRASTIVE LEARNING |
d226965048 | Humans can quickly associate stimuli to solve problems in novel contexts. Our novel neural network model learns state representations of facts that can be composed to perform such associative inference. To this end, we augment the LSTM model with an associative memory, dubbed Fast Weight Memory (FWM). Through different... | Work in progress. Under review. LEARNING ASSOCIATIVE INFERENCE USING FAST WEIGHT MEMORY |
d256231485 | Performant Convolutional Neural Network (CNN) architectures must be tailored to specific tasks in order to consider the length, resolution, and dimensionality of the input data. In this work, we tackle the need for problem-specific CNN architectures. We present the Continuous Convolutional Neural Network (CCNN): a sing... | Published as a conference paper at ICLR 2023 MODELLING LONG RANGE DEPENDENCIES IN N D: FROM TASK-SPECIFIC TO A GENERAL PURPOSE CNN |
d253080510 | Knowledge distillation is one of the primary methods of transferring knowledge from large to small models. However, it requires massive task-specific data, which may not be plausible in many real-world applications. Data augmentation methods such as representation interpolation, token replacement, or augmentation with ... | Published as a conference paper at ICLR 2023 AUGMENTATION WITH PROJECTION: TOWARDS AN EFFECTIVE AND EFFICIENT DATA AUGMENTATION PARADIGM FOR DISTILLATION |
d249394665 | The long runtime of high-fidelity partial differential equation (PDE) solvers makes them unsuitable for time-critical applications. We propose to accelerate PDE solvers using reduced-order modeling (ROM). Whereas prior ROM approaches reduce the dimensionality of discretized vector fields, our continuous reduced-order m... | Published as a conference paper at ICLR 2023 CROM: CONTINUOUS REDUCED-ORDER MODELING OF PDES USING IMPLICIT NEURAL REPRESENTA- TIONS |
d218502175 | Neural Ordinary Differential Equations (ODEs) are elegant reinterpretations of deep networks where continuous time can replace the discrete notion of depth, ODE solvers perform forward propagation, and the adjoint method enables efficient, constant memory backpropagation. Neural ODEs are universal approximators only wh... | Time Dependence in Non-Autonomous Neural ODEs |
d6041587 | EXTENDED ABSTRACTThe modern incarnation of neural networks, now popularly known as Deep Learning (DL), accomplished record-breaking success in processing diverse kinds of signals -vision, audio, and text. In parallel, strong interest has ensued towards constructing a theory of DL. This paper opens up a group theory bas... | A GROUP THEORETIC PERSPECTIVE ON UNSUPER- VISED DEEP LEARNING * |
d16053675 | Triplet networks are widely used models that are characterized by good performance in classification and retrieval tasks. In this work we propose to train a triplet network by putting it as the discriminator in Generative Adversarial Nets (GANs). We make use of the good capability of representation learning of the disc... | Workshop track -ICLR 2017 TRAINING TRIPLET NETWORKS WITH GAN |
d253117181 | We present a smoothly broken power law functional form (referred to by us as a broken neural scaling law (BNSL)) that accurately models and extrapolates the scaling behaviors of deep neural networks (i.e. how the evaluation metric of interest varies as amount of compute used for training (or inference), number of model... | Published as a conference paper at ICLR 2023 BROKEN NEURAL SCALING LAWS |
d14704855 | Restricted Boltzmann Machines (RBMs) are general unsupervised learning devices to ascertain generative models of data distributions. RBMs are often trained using the Contrastive Divergence learning algorithm (CD), an approximation to the gradient of the data log-likelihood. A simple reconstruction error is often used t... | Stopping Criteria in Contrastive Divergence: Alternatives to the Reconstruction Error |
d247839131 | We consider the problem of sequential robotic manipulation of deformable objects using tools. Previous works have shown that differentiable physics simulators provide gradients to the environment state and help trajectory optimization to converge orders of magnitude faster than model-free reinforcement learning algorit... | DIFFSKILL: SKILL ABSTRACTION FROM DIFFEREN- TIABLE PHYSICS FOR DEFORMABLE OBJECT MANIP- ULATIONS WITH TOOLS |
d257353677 | Intent detection with semantically similar fine-grained intents is a challenging task. To address it, we reformulate intent detection as a question-answering retrieval task by treating utterances and intent names as questions and answers. To that end, we utilize a question-answering retrieval architecture and adopt a t... | Published as a conference paper at ICLR 2023 QAID: QUESTION ANSWERING INSPIRED FEW-SHOT INTENT DETECTION |
d207852415 | Recent trends of incorporating attention mechanisms in vision have led researchers to reconsider the supremacy of convolutional layers as a primary building block. Beyond helping CNNs to handle long-range dependencies,Ramachandran et al. (2019)showed that attention can completely replace convolution and achieve state-o... | Published as a conference paper at ICLR 2020 ON THE RELATIONSHIP BETWEEN SELF-ATTENTION AND CONVOLUTIONAL LAYERS |
d5919904 | Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coherent video data. We focus on feature learning from unlabeled video data, using the assumption that adjacent video frames contain semantically... | UNSUPERVISED FEATURE LEARNING FROM TEMPO- RAL DATA |
d7071211 | Machine learning and deep learning in particular has advanced tremendously on perceptual tasks in recent years. However, it remains vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to a human. In this work, we propose to aug... | Published as a conference paper at ICLR 2017 ON DETECTING ADVERSARIAL PERTURBATIONS |
d1896402 | Discourse relations bind smaller linguistic elements into coherent texts. However, automatically identifying discourse relations is difficult, because it requires understanding the semantics of the linked sentences. A more subtle challenge is that it is not enough to represent the meaning of each sentence of a discours... | ENTITY-AUGMENTED DISTRIBUTIONAL SEMANTICS FOR DISCOURSE RELATIONS |
d252907242 | Recent text-to-image generation models have shown promising results in generating high-fidelity photo-realistic images. Though the results are astonishing to human eyes, how applicable these generated images are for recognition tasks remains under-explored. In this work, we extensively study whether and how synthetic i... | Published as a conference paper at ICLR 2023 IS SYNTHETIC DATA FROM GENERATIVE MODELS READY FOR IMAGE RECOGNITION? |
d246063533 | The ability to discover behaviours from past experience and transfer them to new tasks is a hallmark of intelligent agents acting sample-efficiently in the real world. Equipping embodied reinforcement learners with the same ability may be crucial for their successful deployment in robotics. While hierarchical and KL-re... | Published as a conference paper at ICLR 2023 PRIORS, HIERARCHY, AND INFORMATION ASYMMETRY FOR SKILL TRANSFER IN REINFORCEMENT LEARNING |
d12200731 | A standard approach to Collaborative Filtering (CF), i.e. prediction of user ratings on items, relies on Matrix Factorization techniques. Representations for both users and items are computed from the observed ratings and used for prediction. Unfortunatly, these transductive approaches cannot handle the case of new use... | REPRESENTATION LEARNING FOR COLD-START RECOMMENDATION |
d231924480 | The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improving data privacy. In most cases, the assumption of independent and identically distributed samples across local clients does not hold for fe... | Published as a conference paper at ICLR 2021 FEDBN: FEDERATED LEARNING ON NON-IID FEATURES VIA LOCAL BATCH NORMALIZATION |
d211532533 | Fine-tuning from pre-trained ImageNet models has become the de-facto standard for various computer vision tasks. Current practices for fine-tuning typically involve selecting an ad-hoc choice of hyperparameters and keeping them fixed to values normally used for training from scratch. This paper re-examines several comm... | Published as a conference paper at ICLR 2020 RETHINKING THE HYPERPARAMETERS FOR FINE-TUNING |
d232240418 | Recent research in dynamic convolution shows substantial performance boost for efficient CNNs, due to the adaptive aggregation of K static convolution kernels. It has two limitations: (a) it increases the number of convolutional weights by Ktimes, and (b) the joint optimization of dynamic attention and static convoluti... | Published as a conference paper at ICLR 2021 REVISITING DYNAMIC CONVOLUTION VIA MATRIX DECOMPOSITION |
d6981893 | The ability to plan and execute goal specific actions in varied and unseen environments settings is a central requirement of intelligent agents. In this paper, we explore how an agent can be equipped with an internal model of the dynamics of the external world, and how it can use this model to plan novel actions by run... | LEARNING VISUAL PREDICTIVE MODELS OF PHYSICS FOR PLAYING BILLIARDS |
d247083956 | How can we measure the reasoning capabilities of intelligence systems? Visual question answering provides a convenient framework for testing the model's abilities by interrogating the model through questions about the scene. However, despite scores of various visual QA datasets and architectures, which sometimes yield ... | Published as a conference paper at ICLR 2022 MEASURING CLEVRNESS: BLACK-BOX TESTING OF VISUAL REASONING MODELS |
d252780503 | Warning: this paper contains model outputs exhibiting offensiveness and biases.Recently pre-trained language models (PLMs) have prospered in various natural language generation (NLG) tasks due to their ability to generate fairly fluent text. Nevertheless, these models are observed to capture and reproduce harmful conte... | Published as a conference paper at ICLR 2023 UNIFIED DETOXIFYING AND DEBIASING IN LAN- GUAGE GENERATION VIA INFERENCE-TIME ADAPTIVE OPTIMIZATION |
d231812484 | Interpretation of Deep Neural Networks (DNNs) training as an optimal control problem with nonlinear dynamical systems has received considerable attention recently, yet the algorithmic development remains relatively limited. In this work, we make an attempt along this line by reformulating the training procedure from th... | Published as a conference paper at ICLR 2021 DDPNOPT: DIFFERENTIAL DYNAMIC PROGRAMMING NEURAL OPTIMIZER |
d257255438 | Pre-training representations (a.k.a. foundation models) has recently become a prevalent learning paradigm, where one first pre-trains a representation using large-scale unlabeled data, and then learns simple predictors on top of the representation using small labeled data from the downstream tasks. There are two key de... | THE TRADE-OFF BETWEEN UNIVERSALITY AND LA- BEL EFFICIENCY OF REPRESENTATIONS FROM CON- TRASTIVE LEARNING |
d56657907 | We investigate a variant of variational autoencoders where there is a superstructure of discrete latent variables on top of the latent features. In general, our superstructure is a tree structure of multiple super latent variables and it is automatically learned from data. When there is only one latent variable in the ... | LEARNING LATENT SUPERSTRUCTURES IN VARIA- TIONAL AUTOENCODERS FOR DEEP MULTIDIMEN- SIONAL CLUSTERING |
d235614315 | We propose a novel federated learning method for distributively training neural network models, where the server orchestrates cooperation between a subset of randomly chosen devices in each round. We view Federated Learning problem primarily from a communication perspective and allow more device level computations to s... | Published as a conference paper at ICLR 2021 FEDERATED LEARNING BASED ON DYNAMIC REGULARIZATION |
d3649599 | We present a Neural Program Search, an algorithm to generate programs from natural language description and a small number of input / output examples. The algorithm combines methods from Deep Learning and Program Synthesis fields by designing rich domain-specific language (DSL) and defining efficient search algorithm g... | Workshop track -ICLR 2018 Neural Program Search: Solving Programming Tasks from Description and Examples |
d246275797 | Combinatorial optimization lies at the core of many real-world problems. Especially since the rise of graph neural networks (GNNs), the deep learning community has been developing solvers that derive solutions to NP-hard problems by learning the problem-specific solution structure. However, reproducing the results of t... | Published as a conference paper at ICLR 2022 WHAT'S WRONG WITH DEEP LEARNING IN TREE SEARCH FOR COMBINATORIAL OPTIMIZATION |
d3649804 | This paper presents a framework to tackle combinatorial optimization problems using neural networks and reinforcement learning. We focus on the traveling salesman problem (TSP) and train a recurrent neural network that, given a set of city coordinates, predicts a distribution over different city permutations. Using neg... | NEURAL COMBINATORIAL OPTIMIZATION WITH REINFORCEMENT LEARNING |
d212859361 | Over-fitting and over-smoothing are two main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification. In particular, over-fitting weakens the generalization ability on small dataset, while over-smoothing impedes model training by isolating output representations from the input features ... | DROPEDGE: TOWARDS DEEP GRAPH CONVOLU- TIONAL NETWORKS ON NODE CLASSIFICATION |
d3514022 | Model-free reinforcement learning (RL) is a powerful, general tool for learning complex behaviors. However, its sample efficiency is often impractically large for solving challenging real-world problems, even with off-policy algorithms such as Q-learning. A limiting factor in classic model-free RL is that the learning ... | Published as a conference paper at ICLR 2018 TEMPORAL DIFFERENCE MODELS: MODEL-FREE DEEP RL FOR MODEL-BASED CONTROL |
d252683963 | Despite the widespread use of unsupervised models, very few methods are designed to explain them. Most explanation methods explain a scalar model output. However, unsupervised models output representation vectors, the elements of which are not good candidates to explain because they lack semantic meaning. To bridge thi... | Published as a conference paper at ICLR 2023 CONTRASTIVE CORPUS ATTRIBUTION FOR EXPLAIN- ING REPRESENTATIONS |
d252668812 | Many Graph Neural Networks (GNNs) perform poorly compared to simple heuristics on Link Prediction (LP) tasks. This is due to limitations in expressive power such as the inability to count triangles (the backbone of most LP heuristics) and because they can not distinguish automorphic nodes (those having identical struct... | Graph Neural Networks for Link Prediction with Subgraph Sketching Charm Therapeutics |
d211126925 | While deep neural networks have achieved impressive performance on a range of NLP tasks, these data-hungry models heavily rely on labeled data, which restricts their applications in scenarios where data annotation is expensive. Natural language (NL) explanations have been demonstrated very useful additional supervision... | Published as a conference paper at ICLR 2020 LEARNING FROM EXPLANATIONS WITH NEURAL EXECUTION TREE |
d257496463 | Non-autoregressive translation (NAT) reduces the decoding latency but suffers from performance degradation due to the multi-modality problem. Recently, the structure of directed acyclic graph has achieved great success in NAT, which tackles the multi-modality problem by introducing dependency between vertices. However,... | Published as a conference paper at ICLR 2023 FUZZY ALIGNMENTS IN DIRECTED ACYCLIC GRAPH FOR NON-AUTOREGRESSIVE MACHINE TRANSLATION |
d6856808 | Deep neural networks can be obscenely wasteful. When processing video, a convolutional network expends a fixed amount of computation for each frame with no regard to the similarity between neighbouring frames. As a result, it ends up repeatedly doing very similar computations. To put an end to such waste, we introduce ... | SIGMA-DELTA QUANTIZED NETWORKS |
d18233038 | Convolutional networks are one of the most widely employed architectures in computer vision and machine learning. In order to leverage their ability to learn complex functions, large amounts of data are required for training. Training a large convolutional network to produce state-of-the-art results can take weeks, eve... | Fast Training of Convolutional Networks through FFTs |
d259298732 | Learning policies from previously recorded data is a promising direction for realworld robotics tasks, as online learning is often infeasible. Dexterous manipulation in particular remains an open problem in its general form. The combination of offline reinforcement learning with large diverse datasets, however, has the... | Published as a conference paper at ICLR 2023 BENCHMARKING OFFLINE REINFORCEMENT LEARNING ON REAL-ROBOT HARDWARE |
d3476061 | For fast and energy-efficient deployment of trained deep neural networks on resource-constrained embedded hardware, each learned weight parameter should ideally be represented and stored using a single bit. Error-rates usually increase when this requirement is imposed. Here, we report large improvements in error rates ... | Published as a conference paper at ICLR 2018 TRAINING WIDE RESIDUAL NETWORKS FOR DEPLOY- MENT USING A SINGLE BIT FOR EACH WEIGHT |
d251280102 | Normalizing flows are tractable density models that can approximate complicated target distributions, e.g. Boltzmann distributions of physical systems. However, current methods for training flows either suffer from mode-seeking behavior, use samples from the target generated beforehand by expensive MCMC methods, or use... | Published as a conference paper at ICLR 2023 FLOW ANNEALED IMPORTANCE SAMPLING BOOTSTRAP |
d3714278 | Several deep learning models have been proposed for question answering. However, due to their single-pass nature, they have no way to recover from local maxima corresponding to incorrect answers. To address this problem, we introduce the Dynamic Coattention Network (DCN) for question answering. The DCN first fuses co-d... | Published as a conference paper at ICLR 2017 DYNAMIC COATTENTION NETWORKS FOR QUESTION ANSWERING |
d17272965 | Despite the widespread practical success of deep learning methods, our theoretical understanding of the dynamics of learning in deep neural networks remains quite sparse. We attempt to bridge the gap between the theory and practice of deep learning by systematically analyzing learning dynamics for the restricted case o... | Exact solutions to the nonlinear dynamics of learning in deep linear neural networks |
d252595924 | We develop ShiftMatch 1 , a new training-data-dependent likelihood for robustness to corruption in Bayesian neural networks (BNNs). ShiftMatch is inspired by the training-data-dependent "EmpCov" priors fromIzmailov et al. (2021a), and efficiently matches test-time spatial correlations to those at training time. Critica... | Published as a conference paper at ICLR 2023 ROBUSTNESS TO CORRUPTION IN PRE-TRAINED BAYESIAN NEURAL NETWORKS |
d246705937 | Non-stationarity is one thorny issue in cooperative multi-agent reinforcement learning (MARL). One of the reasons is the policy changes of agents during the learning process. Some existing works have discussed various consequences caused by non-stationarity with several kinds of measurement indicators. This makes the o... | Published as a conference paper at ICLR 2022 DEALING WITH NON-STATIONARITY IN MARL VIA TRUST-REGION DECOMPOSITION |
d209500529 | Adaptive gradient algorithms perform gradient-based updates using the history of gradients and are ubiquitous in training deep neural networks. While adaptive gradient methods theory is well understood for minimization problems, the underlying factors driving their empirical success in min-max problems such as GANs rem... | TOWARDS BETTER UNDERSTANDING OF ADAPTIVE GRADIENT ALGORITHMS IN GENERATIVE ADVER- SARIAL NETS |
d5212627 | In this work we perform outlier detection using ensembles of neural networks obtained by variational approximation of the posterior in a Bayesian neural network setting. The variational parameters are obtained by sampling from the true posterior by gradient descent. We show our outlier detection results are comparable ... | EFFICIENT VARIATIONAL BAYESIAN NEURAL NET- WORK ENSEMBLES FOR OUTLIER DETECTION |
d259212405 | Data heterogeneity is one of the most challenging issues in federated learning, which motivates a variety of approaches to learn personalized models for participating clients. One such approach in deep neural networks based tasks is employing a shared feature representation and learning a customized classifier head for... | Published as a conference paper at ICLR 2023 PERSONALIZED FEDERATED LEARNING WITH FEATURE ALIGNMENT AND CLASSIFIER COLLABORATION |
d257405475 | Causal representation learning is the task of identifying the underlying causal variables and their relations from high-dimensional observations, such as images. Recent work has shown that one can reconstruct the causal variables from temporal sequences of observations under the assumption that there are no instantaneo... | Published as a conference paper at ICLR 2023 CAUSAL REPRESENTATION LEARNING FOR INSTANTANEOUS AND TEMPORAL EFFECTS IN INTERACTIVE SYSTEMS |
d226226934 | Complex, multi-task problems have proven to be difficult to solve efficiently in a sparse-reward reinforcement learning setting. In order to be sample efficient, multi-task learning requires reuse and sharing of low-level policies. To facilitate the automatic decomposition of hierarchical tasks, we propose the use of s... | Published as a conference paper at ICLR 2021 ASK YOUR HUMANS: USING HUMAN INSTRUCTIONS TO IMPROVE GENERALIZATION IN REINFORCEMENT LEARNING |
d338016 | CMF is a technique for simultaneously learning low-rank representations based on a collection of matrices with shared entities. A typical example is the joint modeling of useritem, item-property, and user-feature matrices in a recommender system. The key idea in CMF is that the embeddings are shared across the matrices... | Group-sparse Embeddings in Collective Matrix Factorization |
d231846909 | Adversarial patches pose a realistic threat model for physical world attacks on autonomous systems via their perception component. Autonomous systems in safety-critical domains such as automated driving should thus contain a fail-safe fallback component that combines certifiable robustness against patches with efficien... | Published as a conference paper at ICLR 2021 EFFICIENT CERTIFIED DEFENSES AGAINST PATCH AT- TACKS ON IMAGE CLASSIFIERS |
d3303815 | Planning problems in partially observable environments cannot be solved directly with convolutional networks and require some form of memory. But, even memory networks with sophisticated addressing schemes are unable to learn intelligent reasoning satisfactorily due to the complexity of simultaneously learning to acces... | Under review as a conference paper at ICLR 2018 MEMORY AUGMENTED CONTROL NETWORKS |
d252693220 | Distilling knowledge from a large teacher model to a lightweight one is a widely successful approach for generating compact, powerful models in the semi-supervised learning setting where a limited amount of labeled data is available. In large-scale applications, however, the teacher tends to provide a large number of i... | Published as a conference paper at ICLR 2023 ROBUST ACTIVE DISTILLATION |
d211132970 | Learned joint representations of images and text form the backbone of several important cross-domain tasks such as image captioning. Prior work mostly maps both domains into a common latent representation in a purely supervised fashion. This is rather restrictive, however, as the two domains follow distinct generative ... | Published as a conference paper at ICLR 2020 LATENT NORMALIZING FLOWS FOR MANY-TO-MANY CROSS-DOMAIN MAPPINGS |
d253098700 | While large language models (LLMs) like GPT-3 have achieved impressive results on multiple choice question answering (MCQA) tasks in the zero, one, and few-shot settings, they generally lag behind the MCQA state of the art (SOTA). MCQA tasks have traditionally been presented to LLMs like cloze tasks. An LLM is conditio... | Published as a conference paper at ICLR 2023 LEVERAGING LARGE LANGUAGE MODELS FOR MULTIPLE CHOICE QUESTION ANSWERING |
d53158404 | This paper addresses the problem of incremental domain adaptation (IDA) in natural language processing (NLP). We assume each domain comes one after another, and that we could only access data in the current domain. The goal of IDA is to build a unified model performing well on all the domains that we have encountered. ... | Published as a conference paper at ICLR 2020 PROGRESSIVE MEMORY BANKS FOR INCREMENTAL DOMAIN ADAPTATION |
d9864100 | Learning a natural language interface for database tables is a challenging task that involves deep language understanding and multi-step reasoning. The task is often approached by mapping natural language queries to logical forms or programs that provide the desired response when executed on the database. To our knowle... | Published as a conference paper at ICLR 2017 LEARNING A NATURAL LANGUAGE INTERFACE WITH NEURAL PROGRAMMER |
d2684987 | We introduce a parametric nonlinear transformation that is well-suited for Gaussianizing data from natural images. The data are linearly transformed, and each component is then normalized by a pooled activity measure, computed by exponentiating a weighted sum of rectified and exponentiated components and a constant. We... | DENSITY MODELING OF IMAGES USING A GENERALIZED NORMALIZATION TRANSFORMATION |
d14480911 | Optimization by stochastic gradient descent is an important component of many large-scale machine learning algorithms. A wide variety of such optimization algorithms have been devised; however, it is unclear whether these algorithms are robust and widely applicable across many different optimization landscapes. In this... | Unit Tests for Stochastic Optimization |
d231632202 | Learning from a limited number of samples is challenging since the learned model can easily become overfitted based on the biased distribution formed by only a few training examples. In this paper, we calibrate the distribution of these fewsample classes by transferring statistics from the classes with sufficient examp... | Published as a conference paper at ICLR 2021 FREE LUNCH FOR FEW-SHOT LEARNING: DISTRIBUTION CALIBRATION |
d2428314 | Categorical variables are a natural choice for representing discrete structure in the world. However, stochastic neural networks rarely use categorical latent variables due to the inability to backpropagate through samples. In this work, we present an efficient gradient estimator that replaces the non-differentiable sa... | Published as a conference paper at ICLR 2017 CATEGORICAL REPARAMETERIZATION WITH GUMBEL-SOFTMAX |
d235765481 | We propose to address quadrupedal locomotion tasks using Reinforcement Learning (RL) with a Transformer-based model that learns to combine proprioceptive information and high-dimensional depth sensor inputs. While learning-based locomotion has made great advances using RL, most methods still rely on domain randomizatio... | LEARNING VISION-GUIDED QUADRUPEDAL LOCO- MOTION END-TO-END WITH CROSS-MODAL TRANS- FORMERS |
d224470441 | Recently, Neural Topic Models (NTMs) inspired by variational autoencoders have obtained increasingly research interest due to their promising results on text analysis. However, it is usually hard for existing NTMs to achieve good document representation and coherent/diverse topics at the same time. Moreover, they often... | Published as a conference paper at ICLR 2021 NEURAL TOPIC MODEL VIA OPTIMAL TRANSPORT |
d246430569 | Our work focuses on the development of a learnable neural representation of human pose for advanced AI assisted animation tooling. Specifically, we tackle the problem of constructing a full static human pose based on sparse and variable user inputs (e.g. locations and/or orientations of a subset of body joints). To sol... | Published as a conference paper at ICLR 2022 PROTORES: PROTO-RESIDUAL NETWORK FOR POSE AUTHORING VIA LEARNED INVERSE KINEMATICS |
d204960946 | Translation into morphologically-rich languages challenges neural machine translation (NMT) models with extremely sparse vocabularies where atomic treatment of surface forms is unrealistic. This problem is typically addressed by either pre-processing words into subword units or performing translation directly at the le... | A LATENT MORPHOLOGY MODEL FOR OPEN- VOCABULARY NEURAL MACHINE TRANSLATION |
d258833259 | Probabilistic logical rule learning has shown great strength in logical rule mining and knowledge graph completion. It learns logical rules to predict missing edges by reasoning on existing edges in the knowledge graph. However, previous efforts have largely been limited to only modeling chain-like Horn clauses such as... | LOGICAL ENTITY REPRESENTATION IN KNOWLEDGE- GRAPHS FOR DIFFERENTIABLE RULE LEARNING |
d30043042 | A generative model is developed for deep (multi-layered) convolutional dictionary learning. A novel probabilistic pooling operation is integrated into the deep model, yielding efficient bottom-up (pretraining) and top-down (refinement) probabilistic learning. Experimental results demonstrate powerful capabilities of th... | A GENERATIVE MODEL FOR DEEP CONVOLUTIONAL LEARNING |
d254018220 | The rise of generalist large-scale models in natural language and vision has made us expect that a massive data-driven approach could achieve broader generalization in other domains such as continuous control. In this work, we explore a method for learning a single policy that manipulates various forms of agents to sol... | A SYSTEM FOR MORPHOLOGY-TASK GENERALIZA- TION VIA UNIFIED REPRESENTATION AND BEHAVIOR DISTILLATION |
d209501080 | In this work we study locality and compositionality in the context of learning representations for Zero Shot Learning (ZSL). In order to well-isolate the importance of these properties in learned representations, we impose the additional constraint that, differently from most recent work in ZSL, no pre-training on diff... | Published as a conference paper at ICLR 2020 LOCALITY AND COMPOSITIONALITY IN ZERO-SHOT LEARNING |
d231632629 | Recently proposed consistency-based Semi-Supervised Learning (SSL) methods such as the Π-model, temporal ensembling, the mean teacher, or the virtual adversarial training, have advanced the state of the art in several SSL tasks. These methods can typically reach performances that are comparable to their fully supervise... | ON DATA-AUGMENTATION AND CONSISTENCY- BASED SEMI-SUPERVISED LEARNING |
d250425754 | In this paper, we show that recent advances in self-supervised representation learning enable unsupervised object discovery and semantic segmentation with a performance that matches the state of the field on supervised semantic segmentation 10 years ago. We propose a methodology based on unsupervised saliency masks and... | Published as a conference paper at ICLR 2023 UNSUPERVISED SEMANTIC SEGMENTATION WITH SELF-SUPERVISED OBJECT-CENTRIC REPRESENTA- TIONS |
d26100519 | We present Deep Voice 3, a fully-convolutional attention-based neural textto-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training ten times faster. We scale Deep Voice 3 to data set sizes unprecedented for TTS, training on more than eight hundred hours... | DEEP VOICE 3: 2000-SPEAKER NEURAL TEXT-TO-SPEECH |
d16165021 | We develop a new statistical model for photographic images, in which the local responses of a bank of linear filters are described as jointly Gaussian, with zero mean and a covariance that varies slowly over spatial position. We optimize sets of filters so as to minimize the nuclear norm of matrices of their local acti... | THE LOCAL LOW-DIMENSIONALITY OF NATURAL IMAGES |
d231847094 | Sparsity in Deep Neural Networks (DNNs) has been widely studied to compress and accelerate the models on resource-constrained environments. It can be generally categorized into unstructured fine-grained sparsity that zeroes out multiple individual weights distributed across the neural network, and structured coarsegrai... | Published as a conference paper at ICLR 2021 LEARNING N:M FINE-GRAINED STRUCTURED SPARSE NEURAL NETWORKS FROM SCRATCH |
d248863441 | Many policy gradient methods are variants of Actor-Critic (AC), where a value function (critic) is learned to facilitate updating the parameterized policy (actor). The update to the actor involves a log-likelihood update weighted by the actionvalues, with the addition of entropy regularization for soft variants. In thi... | GREEDY ACTOR-CRITIC: A NEW CONDITIONAL CROSS-ENTROPY METHOD FOR POLICY IMPROVE- MENT |
d257663312 | Latent confounding has been a long-standing obstacle for causal reasoning from observational data. One popular approach is to model the data using acyclic directed mixed graphs (ADMGs), which describe ancestral relations between variables using directed and bidirected edges. However, existing methods using AD-MGs are b... | Published as a conference paper at ICLR 2023 CAUSAL REASONING IN THE PRESENCE OF LATENT CONFOUNDERS VIA NEURAL ADMG LEARNING |
d211146346 | Most existing 3D CNNs for video representation learning are clip-based methods, and thus do not consider video-level temporal evolution of spatio-temporal features. In this paper, we propose Video-level 4D Convolutional Neural Networks, referred as V4D, to model the evolution of long-range spatio-temporal representatio... | V4D:4D CONVOLUTIONAL NEURAL NETWORKS FOR VIDEO-LEVEL REPRESENTATION LEARNING |
d232428253 | Off-policy evaluation (OPE) holds the promise of being able to leverage large, offline datasets for both evaluating and selecting complex policies for decision making. The ability to learn offline is particularly important in many real-world domains, such as in healthcare, recommender systems, or robotics, where online... | Published as a conference paper at ICLR 2021 BENCHMARKS FOR DEEP OFF-POLICY EVALUATION |
d14025106 | We describe a mechanism for subsampling sequences and show how to compute its expected output so that it can be trained with standard backpropagation. We test this approach on a simple toy problem and discuss its shortcomings.SUBSAMPLING SEQUENCESConsider a mechanism which, given a sequence of vectors s = {s 0 , s 1 , ... | Workshop track -ICLR 2017 TRAINING A SUBSAMPLING MECHANISM IN EXPECTATION |
d257365796 | Reparameterization aims to improve the generalization of deep neural networks by transforming convolutional layers into equivalent multi-branched structures during training. However, there exists a gap in understanding how reparameterization may change and benefit the learning process of neural networks. In this paper,... | Published as a conference paper at ICLR 2023 REPARAMETERIZATION THROUGH SPATIAL GRADIENT SCALING |
d211838009 | The classical development of neural networks has been primarily for mappings between a finite-dimensional Euclidean space and a set of classes, or between two finite-dimensional Euclidean spaces. The purpose of this work is to generalize neural networks so that they can learn mappings between infinite-dimensional space... | Neural Operator: Graph Kernel Network for Partial Differential Equations |
d235614296 | We aim to help users communicate their intent to machines using flexible, adaptive interfaces that translate arbitrary user input into desired actions. In this work, we focus on assistive typing applications in which a user cannot operate a keyboard, but can instead supply other inputs, such as webcam images that captu... | Published as a conference paper at ICLR 2021 X2T: TRAINING AN X-TO-TEXT TYPING INTERFACE WITH ONLINE LEARNING FROM USER FEEDBACK |
d250280065 | Reliable generalization lies at the heart of safe ML and AI. However, understanding when and how neural networks generalize remains one of the most important unsolved problems in the field. In this work, we conduct an extensive empirical study (20 910 models, 15 tasks) to investigate whether insights from the theory of... | Published as a conference paper at ICLR 2023 NEURAL NETWORKS AND THE CHOMSKY HIERARCHY |
d3458362 | Synthesizing realistic images from text descriptions on a dataset like Microsoft Common Objects in Context (MS COCO), where each image can contain several objects, is a challenging task. Prior work has used text captions to generate images. However, captions might not be informative enough to capture the entire image a... | ChatPainter: Improving Text to Image Generation using Dialogue |
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