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d252407628 | Current abstractive summarization models either suffer from a lack of clear interpretability or provide incomplete rationales by only highlighting parts of the source document. To this end, we propose the Summarization Program (SP), an interpretable modular framework consisting of an (ordered) list of binary trees, eac... | Published as a conference paper at ICLR 2023 SUMMARIZATION PROGRAMS: INTERPRETABLE ABSTRACTIVE SUMMARIZATION WITH NEURAL MODULAR TREES |
d3402524 | Due to the substantial computational cost, training state-of-the-art deep neural networks for large-scale datasets often requires distributed training using multiple computation workers. However, by nature, workers need to frequently communicate gradients, causing severe bottlenecks, especially on lower bandwidth conne... | VARIANCE-BASED GRADIENT COMPRESSION FOR EF- FICIENT DISTRIBUTED DEEP LEARNING |
d247595260 | We propose data-driven one-pass streaming algorithms for estimating the number of triangles and four cycles, two fundamental problems in graph analytics that are widely studied in the graph data stream literature. Recently, Hsu et al. (2019a) and Jiang et al. (2020) applied machine learning techniques in other data ... | Published as a conference paper at ICLR 2022 TRIANGLE AND FOUR CYCLE COUNTING WITH PRE- DICTIONS IN GRAPH STREAMS |
d210472733 | With the success of modern machine learning, it is becoming increasingly important to understand and control how learning algorithms interact. Unfortunately, negative results from game theory show there is little hope of understanding or controlling general n-player games. We therefore introduce smooth markets (SM-game... | Published as a conference paper at ICLR 2020 SMOOTH MARKETS: A BASIC MECHANISM FOR ORGANIZING GRADIENT-BASED LEARNERS |
d248085415 | A challenging problem in task-free continual learning is the online selection of a representative replay memory from data streams. In this work, we investigate the online memory selection problem from an information-theoretic perspective. To gather the most information, we propose the surprise and the learnability crit... | INFORMATION-THEORETIC ONLINE MEMORY SELEC- TION FOR CONTINUAL LEARNING |
d3298378 | Clustering is a cornerstone of unsupervised learning which can be thought as disentangling the multiple generative mechanisms underlying the data. In this paper we introduce an algorithmic framework to train mixtures of implicit generative models which we instantiate for variational autoencoders. Relying on an addition... | Clustering Meets Implicit Generative Models |
d222378211 | Federated learning frameworks have been regarded as a promising approach to break the dilemma between demands on privacy and the promise of learning from large collections of distributed data. Many such frameworks only ask collaborators to share their local update of a common model, i.e. gradients with respect to local... | R-GAP: RECURSIVE GRADIENT ATTACK ON PRIVACY A PREPRINT |
d256416262 | As machine learning (ML) algorithms are increasingly used in high-stakes applications, concerns have arisen that they may be biased against certain social groups. Although many approaches have been proposed to make ML models fair, they typically rely on the assumption that data distributions in training and deployment ... | Published as a conference paper at ICLR 2023 FAIRNESS AND ACCURACY UNDER DOMAIN GENER- ALIZATION |
d249394960 | Deep generative models such as GANs, normalizing flows, and diffusion models are powerful regularizers for inverse problems. They exhibit great potential for helping reduce ill-posedness and attain high-quality results. However, the latent tensors of such deep generative models can fall out of the desired high-dimensio... | DIFFERENTIABLE GAUSSIANIZATION LAYERS FOR INVERSE PROBLEMS REGULARIZED BY DEEP GENER- ATIVE MODELS |
d221995589 | We present GRAPPA, an effective pre-training approach for table semantic parsing that learns a compositional inductive bias in the joint representations of textual and tabular data. We construct synthetic question-SQL pairs over high-quality tables via a synchronous context-free grammar (SCFG). We pre-train GRAPPA on t... | Published as a conference paper at ICLR 2021 GRAPPA: GRAMMAR-AUGMENTED PRE-TRAINING FOR TABLE SEMANTIC PARSING |
d8284678 | In this paper we present a modification to a latent topic model, which makes the model exploit supervision to produce a factorized representation of the observed data. The structured parameterization separately encodes variance that is shared between classes from variance that is private to each class by the introducti... | Factorized Topic Models |
d222272067 | Energy-Based Models (EBMs) present a flexible and appealing way to represent uncertainty. Despite recent advances, training EBMs on high-dimensional data remains a challenging problem as the state-of-the-art approaches are costly, unstable, and require considerable tuning and domain expertise to apply successfully. In ... | Published as a conference paper at ICLR 2021 NO MCMC FOR ME: AMORTIZED SAMPLING FOR FAST AND STABLE TRAINING OF ENERGY-BASED MODELS |
d204008986 | For linear classifiers, the relationship between (normalized) output margin and generalization is captured in a clear and simple bound -a large output margin implies good generalization. Unfortunately, for deep models, this relationship is less clear: existing analyses of the output margin give complicated bounds which... | Improved Sample Complexities for Deep Networks and Robust Classification via an All-Layer Margin |
d5922522 | We show that the image representations in a deep neural network (DNN) can be manipulated to mimic those of other natural images, with only minor, imperceptible perturbations to the original image. Previous methods for generating adversarial images focused on image perturbations designed to produce erroneous class label... | ADVERSARIAL MANIPULATION OF DEEP REPRESENTATIONS |
d14212518 | Regularization is key for deep learning since it allows training more complex models while keeping lower levels of overfitting. However, the most prevalent regularizations do not leverage all the capacity of the models since they rely on reducing the effective number of parameters. Feature decorrelation is an alternati... | Published as a conference paper at ICLR 2017 REGULARIZING CNNS WITH LOCALLY CONSTRAINED DECORRELATIONS |
d208547755 | Learned world models summarize an agent's experience to facilitate learning complex behaviors. While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, there are many potential ways for deriving behaviors from them. We present Dreamer, a reinforcement learning agent t... | Published as a conference paper at ICLR 2020 DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION |
d252355342 | Tabular data is prevalent in many high-stakes domains, such as financial services or public policy. Gradient Boosted Decision Trees (GBDT) are popular in these settings due to their scalability, performance, and low training cost. While fairness in these domains is a foremost concern, existing in-processing Fair ML met... | Published as a conference paper at ICLR 2023 FAIRGBM: GRADIENT BOOSTING WITH FAIRNESS CONSTRAINTS |
d253244506 | Sequence generation applications require satisfying semantic constraints, such as ensuring that programs are correct, using certain keywords, or avoiding undesirable content. Language models, whether fine-tuned or prompted with few-shot demonstrations, frequently violate these constraints, and lack a mechanism to itera... | GENERATING SEQUENCES BY LEARNING TO [SELF-]CORRECT |
d213795117 | This paper shows how to train binary networks to within a few percent points (∼ 3 − 5%) of the full precision counterpart. We first show how to build a strong baseline, which already achieves state-of-the-art accuracy, by combining recently proposed advances and carefully adjusting the optimization procedure. Secondly,... | Published as a conference paper at ICLR 2020 TRAINING BINARY NEURAL NETWORKS WITH REAL- TO-BINARY CONVOLUTIONS |
d15194782 | We stabilize the activations of Recurrent Neural Networks (RNNs) by penalizing the squared distance between successive hidden states' norms. This penalty term is an effective regularizer for RNNs including LSTMs and IRNNs, improving performance on character-level language modeling and phoneme recognition, and outperfor... | Published as a conference paper at ICLR 2016 REGULARIZING RNNS BY STABILIZING ACTIVATIONS |
d246240437 | Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. However, pretrained language models (LM), the foundation of most modern QA systems, do not robustly represent latent relationships between concepts, which is necessary for reasonin... | GREASELM: GRAPH REASONING ENHANCED LANGUAGE MODELS FOR QUESTION ANSWERING |
d256231349 | We present a data-driven, space-time continuous framework to learn surrogate models for complex physical systems described by advection-dominated partial differential equations. Those systems have slow-decaying Kolmogorov n-width that hinders standard methods, including reduced order modeling, from producing high-fidel... | EVOLVE SMOOTHLY, FIT CONSISTENTLY: LEARN- ING SMOOTH LATENT DYNAMICS FOR ADVECTION- DOMINATED SYSTEMS |
d257427539 | Training object detection models usually requires instance-level annotations, such as the positions and labels of all objects present in each image. Such supervision is unfortunately not always available and, more often, only image-level information is provided, also known as weak supervision. Recent works have address... | Published as a conference paper at ICLR 2023 WEAKLY SUPERVISED KNOWLEDGE TRANSFER WITH PROBABILISTIC LOGICAL REASONING FOR OBJECT DETECTION |
d246634100 | Recent research has shown the existence of significant redundancy in large Transformer models. One can prune the redundant parameters without significantly sacrificing the generalization performance. However, we question whether the redundant parameters could have contributed more if they were properly trained. To answ... | Published as a conference paper at ICLR 2022 NO PARAMETERS LEFT BEHIND: SENSITIVITY GUIDED ADAPTIVE LEARNING RATE FOR TRAINING LARGE TRANSFORMER MODELS |
d257365770 | Threshold activation functions are highly preferable in neural networks due to their efficiency in hardware implementations. Moreover, their mode of operation is more interpretable and resembles that of biological neurons. However, traditional gradient based algorithms such as Gradient Descent cannot be used to train t... | GLOBALLY OPTIMAL TRAINING OF NEURAL NET- WORKS WITH THRESHOLD ACTIVATION FUNCTIONS |
d15683369 | This work introduces a transformation-based learner model for classification forests. The weak learner at each split node plays a crucial role in a classification tree. We propose to optimize the splitting objective by learning a linear transformation on subspaces using nuclear norm as the optimization criteria. The le... | Learning Transformations for Classification Forests |
d239010042 | Generative adversarial networks (GANs) with clustered latent spaces can perform conditional generation in a completely unsupervised manner. In the real world, the salient attributes of unlabeled data can be imbalanced. However, most of existing unsupervised conditional GANs cannot cluster attributes of these data in th... | Published as a conference paper at ICLR 2022 STEIN LATENT OPTIMIZATION FOR GENERATIVE ADVERSARIAL NETWORKS |
d235367817 | It has been demonstrated many times that the behavior of the human visual system is connected to the statistics of natural images. Since machine learning relies on the statistics of training data as well, the above connection has interesting implications when using perceptual distances (which mimic the behavior of the ... | Published as a conference paper at ICLR 2022 ON THE RELATION BETWEEN STATISTICAL LEARNING AND PERCEPTUAL DISTANCES |
d250349988 | The ability to continuously acquire new knowledge and skills is crucial for autonomous agents. Existing methods are typically based on either fixed-size models that struggle to learn a large number of diverse behaviors, or growing-size models that scale poorly with the number of tasks. In this work, we aim to strike a ... | Published as a conference paper at ICLR 2023 BUILDING A SUBSPACE OF POLICIES FOR SCALABLE CONTINUAL LEARNING |
d203906040 | We introduce a sparse scattering deep convolutional neural network, which provides a simple model to analyze properties of deep representation learning for classification. Learning a single dictionary matrix with a classifier yields a higher classification accuracy than AlexNet over the ImageNet 2012 dataset. The netwo... | Published as a conference paper at ICLR 2020 DEEP NETWORK CLASSIFICATION BY SCATTERING AND HOMOTOPY DICTIONARY LEARNING |
d252668326 | Over the past few years afterward the birth of ResNet, skip connection has become the defacto standard for the design of modern architectures due to its widespread adoption, easy optimization, and proven performance. Prior work has explained the effectiveness of the skip connection mechanism from different perspectives... | Published as a conference paper at ICLR 2023 RETHINKING SKIP CONNECTION MODEL AS A LEARN- ABLE MARKOV CHAIN |
d1770217 | We introduce a novel method to compute a rank m approximation of the inverse of the Hessian matrix in the distributed regime. By leveraging the differences in gradients and parameters of multiple Workers, we are able to efficiently implement a distributed approximation of the Newton-Raphson method. We also present prel... | Workshop track -ICLR 2017 ACCELERATING SGD FOR DISTRIBUTED DEEP- LEARNING USING APPROXIMTED HESSIAN MATRIXT |
d3533333 | In this paper, we propose to combine imitation and reinforcement learning via the idea of reward shaping using an oracle. We study the effectiveness of the nearoptimal cost-to-go oracle on the planning horizon and demonstrate that the costto-go oracle shortens the learner's planning horizon as function of its accuracy:... | Published as a conference paper at ICLR 2018 TRUNCATED HORIZON POLICY SEARCH: COMBINING REINFORCEMENT LEARNING & IMITATION LEARNING |
d11440692 | Hypernymy, textual entailment, and image captioning can be seen as special cases of a single visual-semantic hierarchy over words, sentences, and images. In this paper we advocate for explicitly modeling the partial order structure of this hierarchy. Towards this goal, we introduce a general method for learning ordered... | ORDER-EMBEDDINGS OF IMAGES AND LANGUAGE |
d222090060 | Graph neural networks (GNNs) have become a popular approach to integrating structural inductive biases into NLP models. However, there has been little work on interpreting them, and specifically on understanding which parts of the graphs (e.g. syntactic trees or co-reference structures) contribute to a prediction. In t... | Published as a conference paper at ICLR 2021 INTERPRETING GRAPH NEURAL NETWORKS FOR NLP WITH DIFFERENTIABLE EDGE MASKING |
d256598350 | Recently multi-lingual pre-trained language models (PLM) such as mBERT and XLM-R have achieved impressive strides in cross-lingual dense retrieval. Despite its successes, they are general-purpose PLM while the multilingual PLM tailored for cross-lingual retrieval is still unexplored. Motivated by an observation that th... | Published as a conference paper at ICLR 2023 MODELING SEQUENTIAL SENTENCE RELATION TO IMPROVE CROSS-LINGUAL DENSE RETRIEVAL |
d252781188 | Offline reinforcement learning (RL) addresses the problem of learning a performant policy from a fixed batch of data collected by following some behavior policy. Model-based approaches are particularly appealing in the offline setting since they can extract more learning signals from the logged dataset by learning a mo... | Published as a conference paper at ICLR 2023 CONSERVATIVE BAYESIAN MODEL-BASED VALUE EXPANSION FOR OFFLINE POLICY OPTIMIZATION |
d14383341 | Restricted Boltzmann machines (RBM) and its variants have become hot research topics recently, and widely applied to many classification problems, such as character recognition and document categorization. Often, classification RBM ignores the interclass relationship or prior knowledge of sharing information among clas... | Restricted Boltzmann Machine for Classification with Hierarchical Correlated Prior |
d246240506 | Improving sample-efficiency and safety are crucial challenges when deploying reinforcement learning in high-stakes real world applications. We propose LAMBDA, a novel model-based approach for policy optimization in safety critical tasks modeled via constrained Markov decision processes. Our approach utilizes Bayesian w... | Published as a conference paper at ICLR 2022 CONSTRAINED POLICY OPTIMIZATION VIA BAYESIAN WORLD MODELS |
d2663445 | Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms f... | Score Function Features for Discriminative Learning: Matrix and Tensor Framework |
d4567927 | A lot of the recent success in natural language processing (NLP) has been driven by distributed vector representations of words trained on large amounts of text in an unsupervised manner. These representations are typically used as general purpose features for words across a range of NLP problems. However, extending th... | Published as a conference paper at ICLR 2018 LEARNING GENERAL PURPOSE DISTRIBUTED SEN- TENCE REPRESENTATIONS VIA LARGE SCALE MULTI- TASK LEARNING |
d252683253 | Prevention of complete and dimensional collapse of representations has recently become a design principle for self-supervised learning (SSL). However, questions remain in our theoretical understanding: When do those collapses occur? What are the mechanisms and causes? We answer these questions by deriving and thoroughl... | Published as a conference paper at ICLR 2023 WHAT SHAPES THE LOSS LANDSCAPE OF SELF SUPER- VISED LEARNING? |
d246679865 | A major challenge in real-world reinforcement learning (RL) is the sparsity of reward feedback. Often, what is available is an intuitive but sparse reward function that only indicates whether the task is completed partially or fully. However, the lack of carefully designed, fine grain feedback implies that most existin... | Published as a conference paper at ICLR 2022 REINFORCEMENT LEARNING WITH SPARSE REWARDS USING GUIDANCE FROM OFFLINE DEMONSTRATION |
d257632202 | In reward-free reinforcement learning (RL), an agent explores the environment first without any reward information, in order to achieve certain learning goals afterwards for any given reward. In this paper we focus on reward-free RL under low-rank MDP models, in which both the representation and linear weight vectors a... | Published as a conference paper at ICLR 2023 IMPROVED SAMPLE COMPLEXITY FOR REWARD- FREE REINFORCEMENT LEARNING UNDER LOW-RANK MDPS |
d3073252 | In this work, we investigate a novel training procedure to learn a generative model as the transition operator of a Markov chain, such that, when applied repeatedly on an unstructured random noise sample, it will denoise it into a sample that matches the target distribution from the training set. The novel training pro... | Published as a conference paper at ICLR 2017 LEARNING TO GENERATE SAMPLES FROM NOISE THROUGH INFUSION TRAINING |
d6626048 | We propose a novel zero-shot learning method for semantic utterance classification (SUC). It learns a classifier f : X → Y for problems where none of the semantic categories Y are present in the training set. The framework uncovers the link between categories and utterances through a semantic space. We show that this s... | Zero-Shot Learning for Semantic Utterance Classification |
d239016913 | We propose StyleNeRF, a 3D-aware generative model for photo-realistic highresolution image synthesis with high multi-view consistency, which can be trained on unstructured 2D images. Existing approaches either cannot synthesize highresolution images with fine details or yield noticeable 3D-inconsistent artifacts. In ad... | STYLENERF: A STYLE-BASED 3D-AWARE GENERA- TOR FOR HIGH-RESOLUTION IMAGE SYNTHESIS |
d7034786 | We propose the neural programmer-interpreter (NPI): a recurrent and compositional neural network that learns to represent and execute programs. NPI has three learnable components: a task-agnostic recurrent core, a persistent key-value program memory, and domain-specific encoders that enable a single NPI to operate in m... | NEURAL PROGRAMMER-INTERPRETERS |
d234357656 | In the mean field regime, neural networks are appropriately scaled so that as the width tends to infinity, the learning dynamics tends to a nonlinear and nontrivial dynamical limit, known as the mean field limit. This lends a way to study large-width neural networks via analyzing the mean field limit. Recent works have... | GLOBAL CONVERGENCE OF THREE-LAYER NEURAL NETWORKS IN THE MEAN FIELD REGIME * |
d257280165 | Unlike current state-of-the-art language models, young children actively acquire language through interactions with their surrounding environment and caretakers. One mechanism that has been argued to be critical to language learning is the ability to infer the mental states of other agents in social environments, coine... | Published as a conference paper at ICLR 2023 COMPUTATIONAL LANGUAGE ACQUISITION WITH THEORY OF MIND |
d232360541 | A broad class of unsupervised deep learning methods such as Generative Adversarial Networks (GANs) involve training of overparameterized models where the number of parameters of the model exceeds a certain threshold. Indeed, most successful GANs used in practice are trained using overparameterized generator and discrim... | Published as a conference paper at ICLR 2021 UNDERSTANDING OVERPARAMETERIZATION IN GENERATIVE ADVERSARIAL NETWORKS |
d235613625 | While deep learning has been very beneficial in data-rich settings, tasks with smaller training set often resort to pre-training or multitask learning to leverage data from other tasks. In this case, careful consideration is needed to select tasks and model parameterizations such that updates from the auxiliary tasks a... | Published as a conference paper at ICLR 2021 AUXILIARY TASK UPDATE DECOMPOSITION: THE GOOD, THE BAD AND THE NEUTRAL |
d211296676 | An open question in the Deep Learning community is why neural networks trained with Gradient Descent generalize well on real datasets even though they are capable of fitting random data. We propose an approach to answering this question based on a hypothesis about the dynamics of gradient descent that we call Coherent ... | Published as a conference paper at ICLR 2020 COHERENT GRADIENTS: AN APPROACH TO UNDERSTANDING GENERALIZATION IN GRADIENT DESCENT-BASED OPTIMIZATION |
d76666188 | Although variational autoencoders (VAEs) represent a widely influential deep generative model, many aspects of the underlying energy function remain poorly understood. In particular, it is commonly believed that Gaussian encoder/decoder assumptions reduce the effectiveness of VAEs in generating realistic samples. In th... | Diagnosing and Enhancing VAE Models Diagnosing and Enhancing VAE Models |
d235613386 | Non-stationarity can arise in Reinforcement Learning (RL) even in stationary environments. For example, most RL algorithms collect new data throughout training, using a non-stationary behaviour policy. Due to the transience of this non-stationarity, it is often not explicitly addressed in deep RL and a single neural ne... | TRANSIENT NON-STATIONARITY AND GENERALISA- TION IN DEEP REINFORCEMENT LEARNING |
d257102476 | Ultra-High-Definition (UHD) photo has gradually become the standard configuration in advanced imaging devices. The new standard unveils many issues in existing approaches for low-light image enhancement (LLIE), especially in dealing with the intricate issue of joint luminance enhancement and noise removal while remaini... | Published as a conference paper at ICLR 2023 EMBEDDING FOURIER FOR ULTRA-HIGH-DEFINITION LOW-LIGHT IMAGE ENHANCEMENT |
d251647177 | bstract This paper introduces a new extragradient-type algorithm for a class of nonconvex-nonconcave minimax problems. It is well-known that finding a local solution for general minimax problems is computationally intractable. This observation has recently motivated the study of structures sufficient for convergence of... | Escaping limit cycles: Global convergence for con- strained nonconvex-nonconcave minimax problems |
d221738974 | Vision-and-language navigation (VLN) is a task in which an agent is embodied in a realistic 3D environment and follows an instruction to reach the goal node. While most of the previous studies have built and investigated a discriminative approach, we notice that there are in fact two possible approaches to building suc... | Generative Language-Grounded Policy in VLN with Bayes' Rule GENERATIVE LANGUAGE-GROUNDED POLICY IN VISION-AND-LANGUAGE NAVIGATION WITH BAYES' RULE |
d231942691 | Recent works have demonstrated reasonable success of representation learning in hypercomplex space. Specifically, "fully-connected layers with Quaternions" (4D hypercomplex numbers), which replace real-valued matrix multiplications in fully-connected layers with Hamilton products of Quaternions, both enjoy parameter sa... | BEYOND FULLY-CONNECTED LAYERS WITH QUATERNIONS: PARAMETERIZATION OF HYPERCOM- PLEX MULTIPLICATIONS WITH 1/n PARAMETERS |
d246473191 | Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labelled data can be difficult to obtain in many applications. Existing approaches typically constrain the target deep neural network (DNN) feature representations to... | AUTO-TRANSFER: LEARNING TO ROUTE TRANSFER- ABLE REPRESENTATIONS |
d16716473 | Spontaneous cortical activity -the ongoing cortical activities in absence of intentional sensory input -is considered to play a vital role in many aspects of both normal brain functions [1] and mental dysfunctions[2]. We present a centered Gaussian-binary Deep Boltzmann Machine (GDBM) for modeling the spontaneous activ... | Modeling correlations in spontaneous activity of visual cortex with centered Gaussian-binary deep Boltzmann machines |
d252668412 | To afford flexible behaviour, the brain must build internal representations that mirror the structure of variables in the external world. For example, 2D space obeys rules: the same set of actions combine in the same way everywhere (step north, then south, and you won't have moved, wherever you start). We suggest the b... | Published as a conference paper at ICLR 2023 ACTIONABLE NEURAL REPRESENTATIONS: GRID CELLS FROM MINIMAL CONSTRAINTS |
d238408467 | Valuation problems, such as feature interpretation, data valuation and model valuation for ensembles, become increasingly more important in many machine learning applications. Such problems are commonly addressed via well-known game-theoretic criteria, such as the Shapley value or Banzhaf value. In this work, we presen... | ENERGY-BASED LEARNING FOR COOPERATIVE GAMES, WITH APPLICATIONS TO VALUATION PROB- LEMS IN MACHINE LEARNING |
d256503835 | Pre-training with offline data and online fine-tuning using reinforcement learning is a promising strategy for learning control policies by leveraging the best of both worlds in terms of sample efficiency and performance. One natural approach is to initialize the policy for online learning with the one trained offline.... | Published as a conference paper at ICLR 2023 POLICY EXPANSION FOR BRIDGING OFFLINE-TO- ONLINE REINFORCEMENT LEARNING |
d3525045 | Generative adversarial networks (GANs) have been extremely effective in approximating complex distributions of high-dimensional, input data samples, and substantial progress has been made in understanding and improving GAN performance in terms of both theory and application. However, we currently lack quantitative meth... | Published as a conference paper at ICLR 2018 QUANTITATIVELY EVALUATING GANS WITH DIVERGENCES PROPOSED FOR TRAINING |
d661332 | Variational inference is a powerful tool for approximate inference, and it has been recently applied for representation learning with deep generative models. We develop the variational Gaussian process (VGP), a Bayesian nonparametric variational family, which adapts its shape to match complex posterior distributions. T... | THE VARIATIONAL GAUSSIAN PROCESS |
d252367603 | Human cognition has compositionality. We understand a scene by decomposing the scene into different concepts (e.g., shape and position of an object) and learning the respective laws of these concepts, which may be either natural (e.g. | Published as a conference paper at ICLR 2023 COMPOSITIONAL LAW PARSING WITH LATENT RANDOM FUNCTIONS |
d251953402 | A well-known failure mode of neural networks is that they may confidently return erroneous predictions. Such unsafe behaviour is particularly frequent when the use case slightly differs from the training context, and/or in the presence of an adversary. This work presents a novel direction to address these issues in a b... | Published as a conference paper at ICLR 2023 CONSTRAINING REPRESENTATIONS YIELDS MODELS THAT KNOW WHAT THEY DON'T KNOW |
d227162254 | Automating molecular design using deep reinforcement learning (RL) has the potential to greatly accelerate the search for novel materials. Despite recent progress on leveraging graph representations to design molecules, such methods are fundamentally limited by the lack of three-dimensional (3D) information. In light o... | Under review SYMMETRY-AWARE ACTOR-CRITIC FOR 3D MOLECULAR DESIGN |
d11130812 | We propose a new method for creating computationally efficient convolutional neural networks (CNNs) by using low-rank representations of convolutional filters. Rather than approximating filters in previously-trained networks with more efficient versions, we learn a set of small basis filters from scratch; during traini... | Published as a conference paper at ICLR 2016 TRAINING CNNS WITH LOW-RANK FILTERS FOR EFFICIENT IMAGE CLASSIFICATION |
d237571392 | Natural language inference (NLI) aims to determine the logical relationship between two sentences, such as Entailment, Contradiction, and Neutral. In recent years, deep learning models have become a prevailing approach to NLI, but they lack interpretability and explainability. In this work, we address the explainabilit... | Published as a conference paper at ICLR 2023 WEAKLY SUPERVISED EXPLAINABLE PHRASAL REASONING WITH NEURAL FUZZY LOGIC |
d3651422 | In this work, we investigate Batch Normalization technique and propose its probabilistic interpretation. We propose a probabilistic model and show that Batch Normalization maximazes the lower bound of its marginalized log-likelihood. Then, according to the new probabilistic model, we design an algorithm which acts cons... | Workshop track -ICLR 2018 UNCERTAINTY ESTIMATION VIA STOCHASTIC BATCH NORMALIZATION |
d234763124 | Decomposing knowledge into interchangeable pieces promises a generalization advantage when there are changes in distribution. A learning agent interacting with its environment is likely to be faced with situations requiring novel combinations of existing pieces of knowledge. We hypothesize that such a decomposition of ... | Published as a conference paper at ICLR 2021 FAST AND SLOW LEARNING OF RECURRENT INDEPEN- DENT MECHANISMS |
d256358497 | Current reinforcement learning (RL) often suffers when solving a challenging exploration problem where the desired outcomes or high rewards are rarely observed. Even though curriculum RL, a framework that solves complex tasks by proposing a sequence of surrogate tasks, shows reasonable results, most of the previous wor... | OUTCOME-DIRECTED REINFORCEMENT LEARNING BY UNCERTAINTY & TEMPORAL DISTANCE-AWARE CURRICULUM GOAL GENERATION |
d210702665 | The selection of initial parameter values for gradient-based optimization of deep neural networks is one of the most impactful hyperparameter choices in deep learning systems, affecting both convergence times and model performance. Yet despite significant empirical and theoretical analysis, relatively little has been p... | PROVABLE BENEFIT OF ORTHOGONAL INITIALIZA- TION IN OPTIMIZING DEEP LINEAR NETWORKS |
d253098926 | High-resolution images are prevalent in various applications, such as autonomous driving and computer-aided diagnosis. However, training neural networks on such images is computationally challenging and easily leads to out-of-memory errors even on modern GPUs. We propose a simple method, Iterative Patch Selection (IPS)... | Published as a conference paper at ICLR 2023 ITERATIVE PATCH SELECTION FOR HIGH-RESOLUTION IMAGE RECOGNITION |
d211842237 | Graph neural networks have recently achieved great successes in predicting quantum mechanical properties of molecules. These models represent a molecule as a graph using only the distance between atoms (nodes). They do not, however, consider the spatial direction from one atom to another, despite directional informatio... | Published as a conference paper at ICLR 2020 DIRECTIONAL MESSAGE PASSING FOR MOLECULAR GRAPHS |
d10034668 | We trained a Siamese network with multi-task same/different information on a speech dataset, and found that it was possible to share a network for both tasks without a loss in performance. The first task was to discriminate between two same or different words, and the second was to discriminate between two same or diff... | Under review as a workshop contribution at ICLR 2015 WEAKLY SUPERVISED MULTI-EMBEDDINGS LEARNING OF ACOUSTIC MODELS |
d195755478 | Machine learning algorithms have been increasingly deployed in critical automated decision-making systems that directly affect human lives. When these algorithms are solely trained to minimize the training/test error, they could suffer from systematic discrimination against individuals based on their sensitive attribut... | Published as a conference paper at ICLR 2020 RÉNYI FAIR INFERENCE |
d247749000 | The study of language emergence aims to understand how human languages are shaped by perceptual grounding and communicative intent. Computational approaches to emergent communication (EC) predominantly consider referential games in limited domains and analyze the learned protocol within the game framework. As a result,... | Published as a conference paper at ICLR 2022 LINKING EMERGENT AND NATURAL LANGUAGES VIA CORPUS TRANSFER |
d219635787 | Modern neural architectures for classification tasks are trained using the crossentropy loss, which is widely believed to be empirically superior to the square loss. In this work we provide evidence indicating that this belief may not be wellfounded. We explore several major neural architectures and a range of standard... | EVALUATION OF NEURAL ARCHITECTURES TRAINED WITH SQUARE LOSS VS CROSS-ENTROPY IN CLASSI- FICATION TASKS |
d257378197 | Learning logical rules is critical to improving reasoning in KGs. This is due to their ability to provide logical and interpretable explanations when used for predictions, as well as their ability to generalize to other tasks, domains, and data. While recent methods have been proposed to learn logical rules, the majori... | Published as a conference paper at ICLR 2023 NEURAL COMPOSITIONAL RULE LEARNING FOR KNOWLEDGE GRAPH REASONING |
d221447955 | Despite the fast development of differentiable architecture search (DARTS), it suffers from a standing instability issue regarding searching performance, which extremely limits its application. Existing robustifying methods draw clues from the outcome instead of finding out the causing factor. Various indicators such a... | DARTS-: ROBUSTLY STEPPING OUT OF PERFOR- MANCE COLLAPSE WITHOUT INDICATORS |
d231662438 | Neural data compression has been shown to outperform classical methods in terms of rate-distortion (RD) performance, with results still improving rapidly. At a high level, neural compression is based on an autoencoder that tries to reconstruct the input instance from a (quantized) latent representation, coupled with a ... | Published as a conference paper at ICLR 2021 OVERFITTING FOR FUN AND PROFIT: INSTANCE-ADAPTIVE DATA COMPRESSION |
d258187051 | Pretrained multilingual large language models have typically used heuristic temperature-based sampling to balance between different languages. However previous work has not systematically evaluated the efficacy of different pretraining language distributions across model scales. In this paper, we propose a new sampling... | Published as a conference paper at ICLR 2023 UNIMAX: FAIRER AND MORE EFFECTIVE LANGUAGE SAMPLING FOR LARGE-SCALE MULTILINGUAL PRE- TRAINING |
d238856948 | While many studies have shown that linguistic information is encoded in hidden word representations, few have studied individual neurons, to show how and in which neurons it is encoded. Among these, the common approach is to use an external probe to rank neurons according to their relevance to some linguistic attribute... | ON THE PITFALLS OF ANALYZING INDIVIDUAL NEU- RONS IN LANGUAGE MODELS |
d238419356 | There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches. In this paper we attempt to evaluate this intuition on various challenging locomotion tasks. We take a hybrid approach, combining model predictive control (MPC) with a learned model a... | EVALUATING MODEL-BASED PLANNING AND PLAN- NER AMORTIZATION FOR CONTINUOUS CONTROL |
d257220219 | Predicting the pose of objects from a single image is an important but difficult computer vision problem. Methods that predict a single point estimate do not predict the pose of objects with symmetries well and cannot represent uncertainty. Alternatively, some works predict a distribution over orientations in SO(3). Ho... | Published as a conference paper at ICLR 2023 IMAGE TO SPHERE: LEARNING EQUIVARIANT FEATURES FOR EFFICIENT POSE PREDICTION |
d231925071 | Structured pruning methods are among the effective strategies for extracting small resource-efficient convolutional neural networks from their dense counterparts with minimal loss in accuracy. However, most existing methods still suffer from one or more limitations, that include 1) the need for training the dense model... | Published as a conference paper at ICLR 2021 CHIPNET: BUDGET-AWARE PRUNING WITH HEAVISIDE CONTINUOUS APPROXIMATIONS |
d213745217 | We investigate multi-task learning approaches that use a shared feature representation for all tasks. To better understand the transfer of task information, we study an architecture with a shared module for all tasks and a separate output module for each task. We study the theory of this setting on linear and ReLU-acti... | Published as a conference paper at ICLR 2020 UNDERSTANDING AND IMPROVING INFORMATION TRANSFER IN MULTI-TASK LEARNING |
d256662371 | Inductive one-bit matrix completion is motivated by modern applications such as recommender systems, where new users would appear at test stage with the ratings consisting of only ones and no zeros. We propose a unified graph signal sampling framework which enjoys the benefits of graph signal analysis and processing. T... | Published as a conference paper at ICLR 2023 GRAPH SIGNAL SAMPLING FOR INDUCTIVE ONE-BIT MATRIX COMPLETION: A CLOSED-FORM SOLUTION |
d258212506 | Model-based neural networks provide unparalleled performance for various tasks, such as sparse coding and compressed sensing problems. Due to the strong connection with the sensing model, these networks are interpretable and inherit prior structure of the problem. In practice, model-based neural networks exhibit higher... | Published as a conference paper at ICLR 2023 GENERALIZATION AND ESTIMATION ERROR BOUNDS FOR MODEL-BASED NEURAL NETWORKS |
d85531885 | Two recently introduced criteria for estimation of generative models are both based on a reduction to binary classification. Noise-contrastive estimation (NCE) is an estimation procedure in which a generative model is trained to be able to distinguish data samples from noise samples. Generative adversarial networks (GA... | ON DISTINGUISHABILITY CRITERIA FOR ESTIMATING GENERATIVE MODELS |
d252762090 | Forward gradient learning computes a noisy directional gradient and is a biologically plausible alternative to backprop for learning deep neural networks. However, the standard forward gradient algorithm, when applied naively, suffers from high variance when the number of parameters to be learned is large. In this pape... | Published as a conference paper at ICLR 2023 SCALING FORWARD GRADIENT WITH LOCAL LOSSES |
d249642402 | Implicit processes (IPs) are a generalization of Gaussian processes (GPs). IPs may lack a closed-form expression but are easy to sample from. Examples include, among others, Bayesian neural networks or neural samplers. IPs can be used as priors over functions, resulting in flexible models with well-calibrated predictio... | Published as a conference paper at ICLR 2023 DEEP VARIATIONAL IMPLICIT PROCESSES |
d257205992 | Generative adversarial networks (GANs), trained on a large-scale image dataset, can be a good approximator of the natural image manifold. GAN-inversion, using a pre-trained generator as a deep generative prior, is a promising tool for image restoration under corruptions. However, the performance of GAN-inversion can be... | Published as a conference paper at ICLR 2023 RGI: ROBUST GAN-INVERSION FOR MASK-FREE IM- AGE INPAINTING AND UNSUPERVISED PIXEL-WISE ANOMALY DETECTION |
d259088578 | Using noisy crowdsourced labels from multiple annotators, a deep learning-based end-to-end (E2E) system aims to learn the label correction mechanism and the neural classifier simultaneously. To this end, many E2E systems concatenate the neural classifier with multiple annotator-specific "label confusion" layers and co-... | Published as a conference paper at ICLR 2023 DEEP LEARNING FROM CROWDSOURCED LABELS: COUPLED CROSS-ENTROPY MINIMIZATION, IDENTI- FIABILITY, AND REGULARIZATION |
d9401721 | This paper presents a novel model for multimodal learning based on gated neural networks. The Gated Multimodal Unit (GMU) model is intended to be used as an internal unit in a neural network architecture whose purpose is to find an intermediate representation based on a combination of data from different modalities. Th... | Workshop track -ICLR 2017 GATED MULTIMODAL UNITS FOR INFORMATION FU- SION |
d232075977 | Spiking neural networks (SNNs) are biology-inspired artificial neural networks (ANNs) that comprise of spiking neurons to process asynchronous discrete signals. While more efficient in power consumption and inference speed on the neuromorphic hardware, SNNs are usually difficult to train directly from scratch with spik... | OPTIMAL CONVERSION OF CONVENTIONAL ARTIFI- CIAL NEURAL NETWORKS TO SPIKING NEURAL NET- WORKS |
d257280094 | All existing 3D-from-2D generators are designed for well-curated single-category datasets, where all the objects have (approximately) the same scale, 3D location and orientation, and the camera always points to the center of the scene. This makes them inapplicable to diverse, in-the-wild datasets of non-alignable scene... | Published as a conference paper at ICLR 2023 3D GENERATION ON IMAGENET |
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