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d222133863 | Graph-structured data are ubiquitous. However, graphs encode diverse types of information and thus play different roles in data representation. In this paper, we distinguish the representational and the correlational roles played by the graphs in node-level prediction tasks, and we investigate how Graph Neural Network ... | COPULAGNN: TOWARDS INTEGRATING REPRESEN- TATIONAL AND CORRELATIONAL ROLES OF GRAPHS IN GRAPH NEURAL NETWORKS |
d3281198 | In this paper, we provide a new neural-network based perspective on multi-task learning (MTL) and multi-domain learning (MDL). By introducing the concept of a semantic descriptor, this framework unifies MDL and MTL as well as encompassing various classic and recent MTL/MDL algorithms by interpreting them as different w... | A UNIFIED PERSPECTIVE ON MULTI-DOMAIN AND MULTI-TASK LEARNING |
d17714865 | There is a practically unlimited amount of natural language data available. Still, recent work in text comprehension has focused on datasets which are small relative to current computing possibilities. This article is making a case for the community to move to larger data and as a step in that direction it is proposing... | Embracing data abundance: BookTest Dataset for Reading Comprehension |
d13619197 | In this paper we propose equipping Generative Adversarial Networks with the ability to produce direct energy estimates for samples. Specifically, we develop a flexible adversarial training framework, and prove this framework not only ensures the generator converges to the true data distribution, but also enables the di... | CALIBRATING ENERGY-BASED GENERATIVE ADVER- SARIAL NETWORKS |
d17684363 | We investigate the use of deep neural networks for the novel task of class-generic object detection. We show that neural networks originally designed for image recognition can be trained to detect objects within images, regardless of their class, including objects for which no bounding box labels have been provided. In... | Deep learning for class-generic object detection |
d211204780 | Off-policy reinforcement learning algorithms promise to be applicable in settings where only a fixed data-set (batch) of environment interactions is available and no new experience can be acquired. This property makes these algorithms appealing for real world problems such as robot control. In practice, however, standa... | Published as a conference paper at ICLR 2020 KEEP DOING WHAT WORKED: BEHAVIOR MODELLING PRIORS FOR OFFLINE REIN- FORCEMENT LEARNING |
d257365515 | Invariant risk minimization (IRM) has received increasing attention as a way to acquire environment-agnostic data representations and predictions, and as a principled solution for preventing spurious correlations from being learned and for improving models' out-of-distribution generalization. Yet, recent works have fou... | Published as a conference paper at ICLR 2023 WHAT IS MISSING IN IRM TRAINING AND EVALUA- TION? CHALLENGES AND SOLUTIONS |
d258461067 | Lottery Ticket Hypothesis (LTH) claims the existence of a winning ticket (i.e., a properly pruned sub-network together with original weight initialization) that can achieve competitive performance to the original dense network. A recent work, called UGS, extended LTH to prune graph neural networks (GNNs) for effectivel... | Published as a conference paper at ICLR 2023 RETHINKING GRAPH LOTTERY TICKETS: GRAPH SPARSITY MATTERS |
d3655946 | Neural text generation models are often autoregressive language models or seq2seq models. These models generate text by sampling words sequentially, with each word conditioned on the previous word, and are state-of-the-art for several machine translation and summarization benchmarks. These benchmarks are often defined ... | MASKGAN: BETTER TEXT GENERATION VIA FILLING IN THE |
d231662383 | We study the robustness of reinforcement learning (RL) with adversarially perturbed state observations, which aligns with the setting of many adversarial attacks to deep reinforcement learning (DRL) and is also important for rolling out real-world RL agent under unpredictable sensing noise. With a fixed agent policy, w... | Published as a conference paper at ICLR 2021 ROBUST REINFORCEMENT LEARNING ON STATE OB- SERVATIONS WITH LEARNED OPTIMAL ADVERSARY |
d251929231 | Many fundamental properties of a quantum system are captured by its Hamiltonian and ground state. Despite the significance, ground states preparation (GSP) is classically intractable for most large-scale Hamiltonians. Quantum neural networks (QNNs), which exert the power of modern quantum machines, have emerged as a le... | Symmetric Pruning in Quantum Neural Net- works |
d209483537 | We propose precision gating (PG), an end-to-end trainable dynamic dual-precision quantization technique for deep neural networks. PG computes most features in a low precision and only a small proportion of important features in a higher precision to preserve accuracy. The proposed approach is applicable to a variety of... | Published as a conference paper at ICLR 2020 PRECISION GATING: IMPROVING NEURAL NETWORK EFFICIENCY WITH DYNAMIC DUAL-PRECISION ACTI- VATIONS |
d166228264 | Adaptive gradient approaches that automatically adjust the learning rate on a perfeature basis have been very popular for training deep networks. This rich class of algorithms includes ADAGRAD, RMSPROP, ADAM, and recent extensions. All these algorithms have adopted diagonal matrix adaptation, due to the prohibitive com... | Stochastic Gradient Methods with Block Diagonal Matrix Adaptation |
d210846466 | This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs. First, we use localized node embeddings computed by a graph neural network to obtain an initial ranking of soft correspondences between nodes. Secondly, we employ synchronous message passing networks ... | Published as a conference paper at ICLR 2020 DEEP GRAPH MATCHING CONSENSUS |
d252873695 | This paper shows that gradient boosting based on symmetric decision trees can be equivalently reformulated as a kernel method that converges to the solution of a certain Kernel Ridge Regression problem. Thus, we obtain the convergence to a Gaussian Process' posterior mean, which, in turn, allows us to easily transform ... | Published as a conference paper at ICLR 2023 GRADIENT BOOSTING PERFORMS GAUSSIAN PROCESS INFERENCE |
d257019961 | The aim of continual learning is to learn new tasks continuously (i.e., plasticity) without forgetting previously learned knowledge from old tasks (i.e., stability). In the scenario of online continual learning, wherein data comes strictly in a streaming manner, the plasticity of online continual learning is more vulne... | Published as a conference paper at ICLR 2023 NEW INSIGHTS FOR THE STABILITY-PLASTICITY DILEMMA IN ONLINE CONTINUAL LEARNING |
d184487860 | Exciting new work on generalization bounds for neural networks (NN) given by Bartlett et al. (2017); Neyshabur et al. (2018) closely depend on two parameterdependant quantities a) the Lipschitz constant upper bound and b) the stable rank (a softer version of rank). Even though these bounds typically have minimal pract... | STABLE RANK NORMALIZATION FOR IMPROVED GEN- ERALIZATION IN NEURAL NETWORKS AND GANS |
d167217322 | Convolution is a central operation in Convolutional Neural Networks (CNNs), which applies a kernel to overlapping regions shifted across the image. However, because of the strong correlations in real-world image data, convolutional kernels are in effect re-learning redundant data. In this work, we show that this redund... | Published as a conference paper at ICLR 2020 NETWORK DECONVOLUTION |
d30535508 | We propose a practical method for L 0 norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularization is interesting since (1) it can greatly speed up training and inference, and (2) it can improve generalization. AIC and BIC, well-known mo... | LEARNING SPARSE NEURAL NETWORKS THROUGH L 0 REGULARIZATION |
d247762270 | It has been well recognized that neural network based image classifiers are easily fooled by images with tiny perturbations crafted by an adversary. There has been a vast volume of research to generate and defend such adversarial attacks. However, the following problem is left unexplored: How to reverse-engineer advers... | REVERSE ENGINEERING OF IMPERCEPTIBLE ADVER- SARIAL IMAGE PERTURBATIONS |
d247761945 | Training convolutional neural networks (CNNs) with a strict Lipschitz constraint under the l 2 norm is useful for provable adversarial robustness, interpretable gradients and stable training. While 1-Lipschitz CNNs can be designed by enforcing a 1-Lipschitz constraint on each layer, training such networks requires each... | |
d254096040 | Score-based modeling through stochastic differential equations (SDEs) has provided a new perspective on diffusion models, and demonstrated superior performance on continuous data. However, the gradient of the log-likelihood function, i.e., the score function, is not properly defined for discrete spaces. This makes it n... | SCORE-BASED CONTINUOUS-TIME DISCRETE DIFFU- SION MODELS |
d202888735 | We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler. Unlike earlier learning-based works that require training the optimizer on the same graph to be optimized, we propose a learning approach that trains an optimizer offline an... | Published as a conference paper at ICLR 2020 REINFORCED GENETIC ALGORITHM LEARNING FOR OPTIMIZING COMPUTATION GRAPHS |
d257050894 | In this work, we provide a deterministic alternative to the stochastic variational training of generative autoencoders. We refer to these new generative autoencoders as AutoEncoders within Flows (AEF), since the encoder and decoder are defined as affine layers of an overall invertible architecture. This results in a de... | Published as a conference paper at ICLR 2023 DETERMINISTIC TRAINING OF GENERATIVE AUTOEN- CODERS USING INVERTIBLE LAYERS |
d235614234 | Attention mechanism in graph neural networks is designed to assign larger weights to important neighbor nodes for better representation. However, what graph attention learns is not understood well, particularly when graphs are noisy. In this paper, we propose a self-supervised graph attention network (SuperGAT), an imp... | Published as a conference paper at ICLR 2021 HOW TO FIND YOUR FRIENDLY NEIGHBORHOOD: GRAPH ATTENTION DESIGN WITH SELF-SUPERVISION |
d4420783 | Understanding and characterizing the subspaces of adversarial examples aid in studying the robustness of deep neural networks (DNNs) to adversarial perturbations. Very recently,Ma et al. (2018)proposed to use local intrinsic dimensionality (LID) in layer-wise hidden representations of DNNs to study adversarial subspace... | Workshop track -ICLR 2018 ON THE LIMITATION OF LOCAL INTRINSIC DIMEN- SIONALITY FOR CHARACTERIZING THE SUBSPACES OF ADVERSARIAL EXAMPLES |
d238583013 | Our goal is to recover time-delayed latent causal variables and identify their relations from measured temporal data. Estimating causally-related latent variables from observations is particularly challenging as the latent variables are not uniquely recoverable in the most general case. In this work, we consider both a... | LEARNING TEMPORALLY CAUSAL LATENT PRO- CESSES FROM GENERAL TEMPORAL DATA |
d247025766 | We are interested in training general-purpose reinforcement learning agents that can solve a wide variety of goals. Training such agents efficiently requires automatic generation of a goal curriculum. This is challenging as it requires (a) exploring goals of increasing difficulty, while ensuring that the agent (b) is e... | IT TAKES FOUR TO TANGO: MULTIAGENT SELFPLAY FOR AUTOMATIC CURRICULUM GENERATION |
d3536221 | Model-free reinforcement learning (RL) methods are succeeding in a growing number of tasks, aided by recent advances in deep learning. However, they tend to suffer from high sample complexity which hinders their use in real-world domains. Alternatively, model-based reinforcement learning promises to reduce sample compl... | MODEL-ENSEMBLE TRUST-REGION POLICY OPTI- MIZATION |
d256358398 | We propose a novel differentiable vortex particle (DVP) method to infer and predict fluid dynamics from a single video. Lying at its core is a particle-based latent space to encapsulate the hidden, Lagrangian vortical evolution underpinning the observable, Eulerian flow phenomena. Our differentiable vortex particles ar... | Published as a conference paper at ICLR 2023 LEARNING VORTEX DYNAMICS FOR FLUID INFERENCE AND PREDICTION |
d256616184 | The robustness of a deep classifier can be characterized by its margins: the decision boundary's distances to natural data points. However, it is unclear whether existing robust training methods effectively increase the margin for each vulnerable point during training. To understand this, we propose a continuous-time f... | Published as a conference paper at ICLR 2023 EXPLORING AND EXPLOITING DECISION BOUNDARY DYNAMICS FOR ADVERSARIAL ROBUSTNESS |
d14469187 | We study the problem of large scale, multi-label visual recognition with a large number of possible classes. We propose a method for augmenting a trained neural network classifier with auxiliary capacity in a manner designed to significantly improve upon an already well-performing model, while minimally impacting its c... | Under review as a workshop contribution at ICLR 2015 SELF-INFORMED NEURAL NETWORK STRUCTURE LEARNING |
d3625614 | This paper presents a novel form of policy gradient for model-free reinforcement learning (RL) with improved exploration properties. Current policy-based methods use entropy regularization to encourage undirected exploration of the reward landscape, which is ineffective in high dimensional spaces with sparse rewards. W... | Published as a conference paper at ICLR 2017 IMPROVING POLICY GRADIENT BY EXPLORING UNDER-APPRECIATED REWARDS |
d236318271 | The Shapley value is one of the most widely used measures of feature importance partly as it measures a feature's average effect on a model's prediction. We introduce joint Shapley values, which directly extend Shapley's axioms and intuitions: joint Shapley values measure a set of features' average contribution to a mo... | JOINT SHAPLEY VALUES: A MEASURE OF JOINT FEA- TURE IMPORTANCE |
d69370873 | We propose a context-adaptive entropy model for use in end-to-end optimized image compression. Our model exploits two types of contexts, bit-consuming contexts and bit-free contexts, distinguished based upon whether additional bit allocation is required. Based on these contexts, we allow the model to more accurately es... | CONTEXT-ADAPTIVE ENTROPY MODEL FOR END-TO- END OPTIMIZED IMAGE COMPRESSION |
d6681692 | This work addresses multi-class segmentation of indoor scenes with RGB-D inputs. While this area of research has gained much attention recently, most works still rely on hand-crafted features. In contrast, we apply a multiscale convolutional network to learn features directly from the images and the depth information. ... | Indoor Semantic Segmentation using depth information |
d252596168 | Low-rank approximation in data streams is a fundamental and significant task in computing science, machine learning and statistics. Multiple streaming algorithms have emerged over years and most of them are inspired by randomized algorithms, more specifically, sketching methods. However, many algorithms are not able to... | Tensor-Based Sketching Method for the Low-Rank Approximation of Data Streams |
d244920855 | Recent works indicate that convolutional neural networks (CNN) need large receptive fields (RF) to compete with visual transformers and their attention mechanism. In CNNs, RFs can simply be enlarged by increasing the convolution kernel sizes. Yet the number of trainable parameters, which scales quadratically with the k... | Published as a conference paper at ICLR 2023 DILATED CONVOLUTION WITH LEARNABLE SPACINGS |
d247939414 | We propose a probabilistic shape completion method extended to the continuous geometry of large-scale 3D scenes. Real-world scans of 3D scenes suffer from a considerable amount of missing data cluttered with unsegmented objects. The problem of shape completion is inherently ill-posed, and high-quality result requires s... | Published as a conference paper at ICLR 2022 PROBABILISTIC IMPLICIT SCENE COMPLETION |
d3654210 | We present a systematic weight pruning framework of deep neural networks (DNNs) using the alternating direction method of multipliers (ADMM). We first formulate the weight pruning problem of DNNs as a constrained nonconvex optimization problem, and then adopt the ADMM framework for systematic weight pruning. We show th... | SYSTEMATIC WEIGHT PRUNING OF DNNS USING ALTERNATING DIRECTION METHOD OF MULTIPLIERS |
d15450945 | In this paper, we investigate a new framework for image classification that adaptively generates spatial representations. Our strategy is based on a sequential process that learns to explore the different regions of any image in order to infer its category. In particular, the choice of regions is specific to each image... | Sequentially Generated Instance-Dependent Image Representations for Classification |
d211146562 | Deep neural networks are known to be annotation-hungry. Numerous efforts have been devoted to reducing the annotation cost when learning with deep networks. Two prominent directions include learning with noisy labels and semi-supervised learning by exploiting unlabeled data. In this work, we propose DivideMix, a novel ... | Published as a conference paper at ICLR 2020 DIVIDEMIX: LEARNING WITH NOISY LABELS AS SEMI-SUPERVISED LEARNING |
d252683964 | Neural Motion Planners (NMPs) have emerged as a promising tool for solving robot navigation tasks in complex environments. However, these methods often require expert data for learning, which limits their application to scenarios where data generation is time-consuming. Recent developments have also led to physicsinfor... | Published as a conference paper at ICLR 2023 NTFIELDS: NEURAL TIME FIELDS FOR PHYSICS- INFORMED ROBOT MOTION PLANNING |
d252968255 | The classical Perceptron algorithm of Rosenblatt can be used to find a linear threshold function to correctly classify n linearly separable data points, assuming the classes are separated by some margin γ > 0. A foundational result is that Perceptron converges after Ω(1/γ 2 ) iterations. There have been several recent ... | On Accelerated Perceptrons and Beyond * |
d246823263 | We consider a context-dependent Reinforcement Learning (RL) setting, which is characterized by: a) an unknown finite number of not directly observable contexts; b) abrupt (discontinuous) context changes occurring during an episode; and c) Markovian context evolution. We argue that this challenging case is often met in ... | Published as a conference paper at ICLR 2022 REINFORCEMENT LEARNING IN PRESENCE OF DIS- CRETE MARKOVIAN CONTEXT EVOLUTION |
d233714270 | To quickly solve new tasks in complex environments, intelligent agents need to build up reusable knowledge. For example, a learned world model captures knowledge about the environment that applies to new tasks. Similarly, skills capture general behaviors that can apply to new tasks. In this paper, we investigate how th... | Published as a conference paper at ICLR 2021 LATENT SKILL PLANNING FOR EXPLORATION AND TRANSFER |
d211126567 | Answering complex logical queries on large-scale incomplete knowledge graphs (KGs) is a fundamental yet challenging task. Recently, a promising approach to this problem has been to embed KG entities as well as the query into a vector space such that entities that answer the query are embedded close to the query. Howeve... | Published as a conference paper at ICLR 2020 QUERY2BOX: REASONING OVER KNOWLEDGE GRAPHS IN VECTOR SPACE USING BOX EMBEDDINGS |
d17801454 | Most representation learning algorithms for language and image processing are local, in that they identify features for a data point based on surrounding points. Yet in language processing, the correct meaning of a word often depends on its global context. As a step toward incorporating global context into representati... | Factorial Hidden Markov Models for Learning Representations of Natural Language |
d239024500 | Offline reinforcement learning (RL) shows promise of applying RL to real-world problems by effectively utilizing previously collected data. Most existing offline RL algorithms use regularization or constraints to suppress extrapolation error for actions outside the dataset. In this paper, we adopt a different framework... | OFFLINE REINFORCEMENT LEARNING WITH VALUE- BASED EPISODIC MEMORY |
d14082107 | This paper provides an extensive study on the availability of image representations based on convolutional networks (ConvNets) for the task of visual instance retrieval. Besides the choice of convolutional layers, we present an efficient pipeline exploiting multi-scale schemes to extract local features, in particular, ... | Paper Visual Instance Retrieval with Deep Convolutional Networks |
d11772257 | We describe a general framework for online adaptation of optimization hyperparameters by 'hot swapping' their values during learning. We investigate this approach in the context of adaptive learning rate selection using an explore-exploit strategy from the multi-armed bandit literature. Experiments on a benchmark neura... | Under review as a workshop contribution at ICLR 2015 HOT SWAPPING FOR ONLINE ADAPTATION OF OPTIMIZATION HYPERPARAMETERS |
d249097739 | Time-dependent data-generating distributions have proven to be difficult for gradient-based training of neural networks, as the greedy updates result in catastrophic forgetting of previously learned knowledge. Despite the progress in the field of continual learning to overcome this forgetting, we show that a set of com... | Published as a conference paper at ICLR 2023 CONTINUAL EVALUATION FOR LIFELONG LEARNING: IDENTIFYING THE STABILITY GAP |
d251643400 | Estimating conditional average treatment effects (CATEs) from observational data is relevant in many fields such as personalized medicine. However, in practice, the treatment assignment is usually confounded by unobserved variables and thus introduces bias. A remedy to remove the bias is the use of instrumental variabl... | Published as a conference paper at ICLR 2023 ESTIMATING INDIVIDUAL TREATMENT EFFECTS UN- DER UNOBSERVED CONFOUNDING USING BINARY IN- STRUMENTS |
d251647801 | The success of state-of-the-art deep neural networks heavily relies on the presence of large-scale labelled datasets, which are extremely expensive and time-consuming to annotate. This paper focuses on tackling semi-supervised part segmentation tasks by generating high-quality images with a pre-trained GAN and labellin... | Published as a conference paper at ICLR 2022 LEARNING TO ANNOTATE PART SEGMENTATION WITH GRADIENT MATCHING |
d249097488 | In this work we propose a Reinforcement Learning (RL) agent that can discover complex behaviours in a rich environment with a simple reward function. We define diversity in terms of state-action occupancy measures, since policies with different occupancy measures visit different states on average. More importantly, def... | Published as a conference paper at ICLR 2023 DISCOVERING POLICIES WITH DOMINO: DIVERSITY OPTIMIZATION MAINTAINING NEAR OPTIMALITY |
d249674757 | We present Masked Frequency Modeling (MFM), a unified frequency-domainbased approach for self-supervised pre-training of visual models. Instead of randomly inserting mask tokens to the input embeddings in the spatial domain, in this paper, we shift the perspective to the frequency domain. Specifically, MFM first masks ... | Published as a conference paper at ICLR 2023 MASKED FREQUENCY MODELING FOR SELF-SUPERVISED VISUAL PRE-TRAINING |
d3520830 | The huge size of deep networks hinders their use in small computing devices. In this paper, we consider compressing the network by weight quantization. We extend a recently proposed loss-aware weight binarization scheme to ternarization, with possibly different scaling parameters for the positive and negative weights, ... | LOSS-AWARE WEIGHT QUANTIZATION OF DEEP NET- WORKS |
d256459705 | As graph data size increases, the vast latency and memory consumption during inference pose a significant challenge to the real-world deployment of Graph Neural Networks (GNNs). While quantization is a powerful approach to reducing GNNs complexity, most previous works on GNNs quantization fail to exploit the unique cha... | A 2 Q: AGGREGATION-AWARE QUANTIZATION FOR GRAPH NEURAL NETWORKS |
d231879554 | Labeled data for imitation learning of theorem proving in large libraries of formalized mathematics is scarce, as such libraries require years of concentrated effort by human specialists to be built. This is particularly challenging when applying large Transformer language models to tactic prediction, because the scali... | Published as a conference paper at ICLR 2022 PROOF ARTIFACT CO-TRAINING FOR THEOREM PROV- ING WITH LANGUAGE MODELS |
d208309981 | A recent line of research on deep learning focuses on the extremely overparameterized setting, and shows that when the network width is larger than a high degree polynomial of the training sample size n and the inverse of the target error ´1 , deep neural networks learned by (stochastic) gradient descent enjoy nice opt... | Published as a conference paper at ICLR 2021 HOW MUCH OVER-PARAMETERIZATION IS SUFFI- CIENT TO LEARN DEEP RELU NETWORKS? |
d231662087 | Identifying harmful instances, whose absence in a training dataset improves model performance, is important for building better machine learning models. Although previous studies have succeeded in estimating harmful instances under supervised settings, they cannot be trivially extended to generative adversarial network... | INFLUENCE ESTIMATION FOR GENERATIVE ADVER- SARIAL NETWORKS |
d258835696 | Neural fields are receiving increased attention as a geometric representation due to their ability to compactly store detailed and smooth shapes and easily undergo topological changes. Compared to classic geometry representations, however, neural representations do not allow the user to exert intuitive control over the... | Published as a conference paper at ICLR 2023 NEURAL IMPLICIT SHAPE EDITING USING BOUNDARY SENSITIVITY |
d231933963 | In this work we consider data-driven optimization problems where one must maximize a function given only queries at a fixed set of points. This problem setting emerges in many domains where function evaluation is a complex and expensive process, such as in the design of materials, vehicles, or neural network architectu... | OFFLINE MODEL-BASED OPTIMIZATION VIA NOR- MALIZED MAXIMUM LIKELIHOOD ESTIMATION |
d254854609 | Deep learning has revolutionized human society, yet the black-box nature of deep neural networks hinders further application to reliability-demanding industries. In the attempt to unpack them, many works observe or impact internal variables to improve the comprehensibility and invertibility of the black-box models. How... | Published as a conference paper at ICLR 2023 BORT: TOWARDS EXPLAINABLE NEURAL NETWORKS WITH BOUNDED ORTHOGONAL CONSTRAINT |
d202889044 | Adversarial training, which minimizes the maximal risk for label-preserving input perturbations, has proved to be effective for improving the generalization of language models. In this work, we propose a novel adversarial training algorithm -FreeLB, that promotes higher robustness and invariance in the embedding space,... | FREELB: ENHANCED ADVERSARIAL TRAINING FOR LANGUAGE UNDERSTANDING |
d16605700 | With machine learning successfully applied to new daunting problems almost every day, general AI starts looking like an attainable goal(LeCun et al., 2015). However, most current research focuses instead on important but narrow applications, such as image classification or machine translation. We believe this to be lar... | COMMAI: EVALUATING THE FIRST STEPS TOWARDS A USEFUL GENERAL AI |
d233386821 | We present a principled approach to incorporating labels in variational autoencoders (VAEs) that captures the rich characteristic information associated with those labels. While prior work has typically conflated these by learning latent variables that directly correspond to label values, we argue this is contrary to t... | Published as a conference paper at ICLR 2021 CAPTURING LABEL CHARACTERISTICS IN VAEs |
d247594436 | Recently, prefix-tuning has gained increasing attention as a parameter-efficient finetuning method for large-scale pretrained language models. The method keeps the pretrained models fixed and only updates the prefix token parameters for each downstream task. Despite being lightweight and modular, prefix-tuning still la... | Published as a conference paper at ICLR 2022 ON ROBUST PREFIX-TUNING FOR TEXT CLASSIFICA- TION |
d212657453 | A common approach to define convolutions on meshes is to interpret them as a graph and apply graph convolutional networks (GCNs). Such GCNs utilize isotropic kernels and are therefore insensitive to the relative orientation of vertices and thus to the geometry of the mesh as a whole. We propose Gauge Equivariant Mesh C... | GAUGE EQUIVARIANT MESH CNNS ANISOTROPIC CONVOLUTIONS ON GEOMETRIC GRAPHS |
d16134629 | We present an approach to sensorimotor control in immersive environments. Our approach utilizes a high-dimensional sensory stream and a lower-dimensional measurement stream. The cotemporal structure of these streams provides a rich supervisory signal, which enables training a sensorimotor control model by interacting w... | Published as a conference paper at ICLR 2017 LEARNING TO ACT BY PREDICTING THE FUTURE |
d252668761 | We tackle the problem of generating audio samples conditioned on descriptive text captions. In this work, we propose AUDIOGEN, an auto-regressive generative model that generates audio samples conditioned on text inputs. AUDIOGEN operates on a learnt discrete audio representation. The task of text-to-audio generation po... | Published as a conference paper at ICLR 2023 AUDIOGEN: TEXTUALLY GUIDED AUDIO GENERA- TION |
d236493649 | Unsupervised skill learning objectives (Eysenbach et al., 2019; Gregor et al., 2016) allow agents to learn rich repertoires of behavior in the absence of extrinsic rewards. They work by simultaneously training a policy to produce distinguishable latent-conditioned trajectories, and a discriminator to evaluate disting... | Published as a conference paper at ICLR 2022 LEARNING MORE SKILLS THROUGH OPTIMISTIC EXPLORATION |
d226236703 | Recent progress in deep learning has enabled many advances in sound separation and visual scene understanding. However, extracting sound sources which are apparent in natural videos remains an open problem. In this work, we present AudioScope, a novel audio-visual sound separation framework that can be trained without ... | Published as a conference paper at ICLR 2021 INTO THE WILD WITH AUDIOSCOPE: UNSUPERVISED AUDIO-VISUAL SEPARATION OF ON-SCREEN SOUNDS |
d3531856 | We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs. As our main theoretical contribution, we clarify the situation with bias in GAN loss functions raised by recent work: we show that gradient estimators used in the optimizat... | Published as a conference paper at ICLR 2018 DEMYSTIFYING MMD GANS |
d231639398 | The embedding-based representation learning is commonly used in deep learning recommendation models to map the raw sparse features to dense vectors. The traditional embedding manner that assigns a uniform size to all features has two issues. First, the numerous features inevitably lead to a gigantic embedding table tha... | Published as a conference paper at ICLR 2021 LEARNABLE EMBEDDING SIZES FOR RECOMMENDER SYSTEMS |
d231698861 | In many domains data is currently represented as graphs and therefore, the graph representation of this data becomes increasingly important in machine learning. Network data is, implicitly or explicitly, always represented using a graph shift operator (GSO) with the most common choices being the adjacency, Laplacian ma... | Published as a conference paper at ICLR 2021 LEARNING PARAMETRISED GRAPH SHIFT OPERATORS |
d2868054 | We present flattened convolutional neural networks that are designed for fast feedforward execution. The redundancy of the parameters, especially weights of the convolutional filters in convolutional neural networks has been extensively studied and different heuristics have been proposed to construct a low rank basis o... | FLATTENED CONVOLUTIONAL NEURAL NETWORKS FOR FEEDFORWARD ACCELERATION |
d6666117 | We propose a unified framework for neural net normalization, regularization and optimization, which includes Path-SGD and Batch-Normalization and interpolates between them across two different dimensions. Through this framework we investigate the issue of invariance of the optimization, data dependence and the connecti... | DATA-DEPENDENT PATH NORMALIZATION IN NEURAL NETWORKS |
d14498604 | Learning invariant representations from images is one of the hardest challenges facing computer vision. Spatial pooling is widely used to create invariance to spatial shifting, but it is restricted to convolutional models. In this paper, we propose a novel pooling method that can learn soft clustering of features from ... | Auto-pooling: Learning to Improve Invariance of Image Features from Image Sequences |
d22191393 | The graph convolutional networks (GCN) recently proposed by Kipf and Welling are an effective graph model for semi-supervised learning. This model, however, was originally designed to be learned with the presence of both training and test data. Moreover, the recursive neighborhood expansion across layers poses time and... | Published as a conference paper at ICLR 2018 FASTGCN: FAST LEARNING WITH GRAPH CONVOLU- TIONAL NETWORKS VIA IMPORTANCE SAMPLING |
d235266218 | Large-batch training has become a commonly used technique when training neural networks with a large number of GPU/TPU processors. As batch size increases, stochastic optimizers tend to converge to sharp local minima, leading to degraded test performance. Current methods usually use extensive data augmentation to incre... | CONCURRENT ADVERSARIAL LEARNING FOR LARGE- BATCH TRAINING |
d1996665 | Deep Convolutional Neural Networks (DCNNs) have recently shown state of the art performance in high level vision tasks, such as image classification and object detection. This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel-level classification (also called "s... | Published as a conference paper at ICLR 2015 SEMANTIC IMAGE SEGMENTATION WITH DEEP CON- VOLUTIONAL NETS AND FULLY CONNECTED CRFS |
d244729433 | Multi-view projection methods have demonstrated promising performance on 3D understanding tasks like 3D classification and segmentation. However, it remains unclear how to combine such multi-view methods with the widely available 3D point clouds. Previous methods use unlearned heuristics to combine features at the poin... | Published as a conference paper at ICLR 2023 Voint Cloud: Multi-View Point Cloud Rep- resentation for 3D Understanding |
d257254835 | Out-of-distribution (OOD) detection is a critical task for reliable machine learning. Recent advances in representation learning give rise to distance-based OOD detection, where testing samples are detected as OOD if they are relatively far away from the centroids or prototypes of in-distribution (ID) classes. However,... | Published as a conference paper at ICLR 2023 HOW TO EXPLOIT HYPERSPHERICAL EMBEDDINGS FOR OUT-OF-DISTRIBUTION DETECTION? |
d208100714 | In this paper we delve deep in the Transformer architecture by investigating two of its core components: self-attention and contextual embeddings. In particular, we study the identifiability of attention weights and token embeddings, and the aggregation of context into hidden tokens. We show that, for sequences longer ... | Published as a conference paper at ICLR 2020 ON IDENTIFIABILITY IN TRANSFORMERS |
d257427168 | Recently, a few self-supervised representation learning (SSL) methods have outperformed the ImageNet classification pre-training for vision tasks such as object detection. However, its effects on 3D human body pose and shape estimation (3DHPSE) are open to question, whose target is fixed to a unique class, the human, a... | RETHINKING SELF-SUPERVISED VISUAL REPRESEN- TATION LEARNING IN PRE-TRAINING FOR 3D HUMAN POSE AND SHAPE ESTIMATION |
d3652033 | Which Generative Adversarial Networks (GANs) generates the most plausible images?" has been a frequently asked question among researchers. To address this problem, we first propose an incomplete U-statistics estimate of maximum mean discrepancy MMD inc to measure the distribution discrepancy between generated and real ... | Workshop track -ICLR 2018 SELECTING THE BEST IN GANS FAMILY: A POST SE- LECTION INFERENCE FRAMEWORK |
d256503666 | Probabilistic dynamics model ensemble is widely used in existing model-based reinforcement learning methods as it outperforms a single dynamics model in both asymptotic performance and sample efficiency. In this paper, we provide both practical and theoretical insights on the empirical success of the probabilistic dyna... | IS MODEL ENSEMBLE NECESSARY? MODEL-BASED RL VIA A SINGLE MODEL WITH LIPSCHITZ REGULAR- IZED VALUE FUNCTION |
d238407870 | Including covariant information, such as position, force, velocity or spin is important in many tasks in computational physics and chemistry. We introduce Steerable E(3) Equivariant Graph Neural Networks (SEGNNs) that generalise equivariant graph networks, such that node and edge attributes are not restricted to invari... | Published as a conference paper at ICLR 2022 GEOMETRIC AND PHYSICAL QUANTITIES IMPROVE E(3) EQUIVARIANT MESSAGE PASSING |
d211126517 | We describe a novel way of representing a symbolic knowledge base (KB) called a sparse-matrix reified KB. This representation enables neural KB inference modules that are fully differentiable, faithful to the original semantics of the KB, expressive enough to model multi-hop inferences, and scalable enough to use with ... | Published as a conference paper at ICLR 2020 SCALABLE NEURAL METHODS FOR REASONING WITH A SYMBOLIC KNOWLEDGE BASE |
d3140413 | Inspired by the principles of speed reading, we introduce Skim-RNN, a recurrent neural network (RNN) that dynamically decides to update only a small fraction of the hidden state for relatively unimportant input tokens. Skim-RNN gives computational advantage over an RNN that always updates the entire hidden state. Skim-... | Under review as a conference paper at ICLR 2018 NEURAL SPEED READING VIA SKIM-RNN |
d2401161 | One of the long-standing challenges in Artificial Intelligence for goal-directed behavior is to build a single agent which can solve multiple tasks. Recent progress in multi-task learning for goal-directed sequential tasks has been in the form of distillation based learning wherein a student network learns from multipl... | Online Multi-Task Learning Using Active Sampling |
d247026059 | In this paper, we study the problem of finding mixed Nash equilibrium for meanfield two-player zero-sum games. Solving this problem requires optimizing over two probability distributions. We consider a quasistatic Wasserstein gradient flow dynamics in which one probability distribution follows the Wasserstein gradient ... | Published as a conference paper at ICLR 2022 PROVABLY CONVERGENT QUASISTATIC DYNAMICS FOR MEAN-FIELD TWO-PLAYER ZERO-SUM GAMES |
d257255267 | The translational equivariant nature of Convolutional Neural Networks (CNNs) is a reason for its great success in computer vision. However, networks do not enjoy more general equivariance properties such as rotation or scaling, ultimately limiting their generalization performance. To address this limitation, we devise ... | Published as a conference paper at ICLR 2023 EMPOWERING NETWORKS WITH SCALE AND ROTA- TION EQUIVARIANCE USING A SIMILARITY CONVO- LUTION |
d254043800 | Neural sequence models, especially transformers, exhibit a remarkable capacity for in-context learning. They can construct new predictors from sequences of labeled examples (x, f (x)) presented in the input without further parameter updates. We investigate the hypothesis that transformer-based in-context learners imple... | Published as a conference paper at ICLR 2023 WHAT LEARNING ALGORITHM IS IN-CONTEXT LEARN- ING? INVESTIGATIONS WITH LINEAR MODELS |
d246411377 | Finding multiple solutions of non-convex optimization problems is a ubiquitous yet challenging task. Most past algorithms either apply single-solution optimization methods from multiple random initial guesses or search in the vicinity of found solutions using ad hoc heuristics. We present an end-to-end method to learn ... | Published as a conference paper at ICLR 2023 LEARNING PROXIMAL OPERATORS TO DISCOVER MULTIPLE OPTIMA |
d211133088 | Learning-based approaches for semantic segmentation have two inherent challenges. First, acquiring pixel-wise labels is expensive and time-consuming. Second, realistic segmentation datasets are highly unbalanced: some categories are much more abundant than others, biasing the performance to the most represented ones. I... | Published as a conference paper at ICLR 2020 REINFORCED ACTIVE LEARNING FOR IMAGE SEGMENTATION |
d239009955 | Deep Generative Networks (DGNs) are extensively employed in Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and their variants to approximate the data manifold and distribution. However, training samples are often distributed in a non-uniform fashion on the manifold, due to costs or convenience... | MaGNET : UNIFORM SAMPLING FROM DEEP GENERA- TIVE NETWORK MANIFOLDS WITHOUT RETRAINING |
d247154647 | This paper addresses the unsupervised learning of content-style decomposed representation. We first give a definition of style and then model the content-style representation as a token-level bipartite graph. An unsupervised framework, named Retriever, is proposed to learn such representations. First, a cross-attention... | Technology of China, 2 HKUST, 3 Microsoft Research Asia |
d256194286 | Deep neural networks (DNNs) are nowadays ubiquitous in many domains such as computer vision. However, due to their high latency, the deployment of DNNs hinges on the development of compression techniques such as quantization which consists in lowering the number of bits used to encode the weights and activations. Growi... | Arxiv version POWERQUANT: AUTOMORPHISM SEARCH FOR NON- UNIFORM QUANTIZATION |
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