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d10296217 | This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environments, including the discrete 57-game Atari domain and several continuous control problems. To achieve this, the paper introduces several inno... | Published as a conference paper at ICLR 2017 SAMPLE EFFICIENT ACTOR-CRITIC WITH EXPERIENCE REPLAY |
d256846667 | Gradient clipping is an important technique for deep neural networks with exploding gradients, such as recurrent neural networks. Recent studies have shown that the loss functions of these networks do not satisfy the conventional smoothness condition, but instead satisfy a relaxed smoothness condition, i.e., the Lipsch... | EPISODE: EPISODIC GRADIENT CLIPPING WITH PE- RIODIC RESAMPLED CORRECTIONS FOR FEDERATED LEARNING WITH HETEROGENEOUS DATA |
d11994930 | We present a probabilistic variant of the recently introduced maxout unit. The success of deep neural networks utilizing maxout can partly be attributed to favorable performance under dropout, when compared to rectified linear units. It however also depends on the fact that each maxout unit performs a pooling operation... | Improving Deep Neural Networks with Probabilistic Maxout Units |
d231933751 | Performing inference on deep learning models for videos remains a challenge due to the large amount of computational resources required to achieve robust recognition. An inherent property of real-world videos is the high correlation of information across frames which can translate into redundancy in either temporal or ... | VA-RED 2 : VIDEO ADAPTIVE REDUNDANCY REDUC- TION |
d211132756 | While recent continual learning methods largely alleviate the catastrophic problem on toy-sized datasets, some issues remain to be tackled to apply them to real-world problem domains. First, a continual learning model should effectively handle catastrophic forgetting and be efficient to train even with a large number o... | Published as a conference paper at ICLR 2020 SCALABLE AND ORDER-ROBUST CONTINUAL LEARN- ING WITH ADDITIVE PARAMETER DECOMPOSITION |
d3280771 | The driving force behind deep networks is their ability to compactly represent rich classes of functions. The primary notion for formally reasoning about this phenomenon is expressive efficiency, which refers to a situation where one network must grow unfeasibly large in order to realize (or approximate) functions of a... | Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions Amnon Shashua |
d252693021 | We consider infinite-horizon discounted Markov decision processes and study the convergence rates of the natural policy gradient (NPG) and the Q-NPG methods with the log-linear policy class. Using the compatible function approximation framework, both methods with log-linear policies can be written as inexact versions o... | Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies * |
d214184365 | Differentiable architecture search (DARTS) provided a fast solution in finding effective network architectures, but suffered from large memory and computing overheads in jointly training a super-network and searching for an optimal architecture. In this paper, we present a novel approach, namely, Partially-Connected DA... | Published as a conference paper at ICLR 2020 PC-DARTS: PARTIAL CHANNEL CONNECTIONS FOR MEMORY-EFFICIENT ARCHITECTURE SEARCH |
d6954272 | Sequence to sequence learning has recently emerged as a new paradigm in supervised learning. To date, most of its applications focused on only one task and not much work explored this framework for multiple tasks. This paper examines three multi-task learning (MTL) settings for sequence to sequence models: (a) the onet... | MULTI-TASK SEQUENCE TO SEQUENCE LEARNING |
d67788344 | Reinforcement learning is a promising framework for solving control problems, but its use in practical situations is hampered by the fact that reward functions are often difficult to engineer. Specifying goals and tasks for autonomous machines, such as robots, is a significant challenge: conventionally, reward function... | FROM LANGUAGE TO GOALS: INVERSE REINFORCE- MENT LEARNING FOR VISION-BASED INSTRUCTION FOLLOWING |
d3604396 | Neural networks are known to be vulnerable to adversarial examples. Carefully chosen perturbations to real images, while imperceptible to humans, induce misclassification and threaten the reliability of deep learning systems in the wild. To guard against adversarial examples, we take inspiration from game theory and ca... | Published as a conference paper at ICLR 2018 STOCHASTIC ACTIVATION PRUNING FOR ROBUST ADVERSARIAL DEFENSE |
d246411466 | We present a novel neural-networks-based algorithm to compute optimal transport maps and plans for strong and weak transport costs. To justify the usage of neural networks, we prove that they are universal approximators of transport plans between probability distributions. We evaluate the performance of our optimal tra... | Published as a conference paper at ICLR 2023 NEURAL OPTIMAL TRANSPORT |
d15310708 | The bag-of-words (BOW) model is the common approach for classifying documents, where words are used as feature for training a classifier. This generally involves a huge number of features. Some techniques, such as Latent Semantic Analysis (LSA) or Latent Dirichlet Allocation (LDA), have been designed to summarize docum... | Under review as a conference paper at ICLR 2015 N-GRAM-BASED LOW-DIMENSIONAL REPRESENTA- TION FOR DOCUMENT CLASSIFICATION |
d235265790 | Clustering is one of the most fundamental tasks in machine learning. Recently, deep clustering has become a major trend in clustering techniques. Representation learning often plays an important role in the effectiveness of deep clustering, and thus can be a principal cause of performance degradation. In this paper, we... | CLUSTERING-FRIENDLY REPRESENTATION LEARN- ING VIA INSTANCE DISCRIMINATION AND FEATURE DECORRELATION |
d18882526 | This paper presents experiments extending the work ofBa et al. (2014)on recurrent neural models for attention into less constrained visual environments, beginning with fine-grained categorization on the Stanford Dogs data set. In this work we use an RNN of the same structure but substitute a more powerful visual networ... | Under review as a workshop contribution at ICLR 2015 ATTENTION FOR FINE-GRAINED CATEGORIZATION |
d9062199 | We introduce a two-layer wavelet scattering network, for object classification. This scattering transform computes a spatial wavelet transform on the first layer and a new joint wavelet transform along spatial, angular and scale variables in the second layer. Numerical experiments demonstrate that this two layer convol... | Generic Deep Networks with Wavelet Scattering |
d258436929 | Simulating the time evolution of physical systems is pivotal in many scientific and engineering problems. An open challenge in simulating such systems is their multi-resolution dynamics: a small fraction of the system is extremely dynamic, and requires very fine-grained resolution, while a majority of the system is cha... | Published as a conference paper at ICLR 2023 LEARNING CONTROLLABLE ADAPTIVE SIMULATION FOR MULTI-RESOLUTION PHYSICS |
d255998264 | Classical wisdom suggests that estimators should avoid fitting noise to achieve good generalization. In contrast, modern overparameterized models can yield small test error despite interpolating noise -a phenomenon often called "benign overfitting" or "harmless interpolation". This paper argues that the degree to which... | Published as a conference paper at ICLR 2023 STRONG INDUCTIVE BIASES PROVABLY PREVENT HARMLESS INTERPOLATION |
d14482228 | This paper introduces a greedy parser based on neural networks, which leverages a new compositional sub-tree representation. The greedy parser and the compositional procedure are jointly trained, and tightly depends on each-other. The composition procedure outputs a vector representation which summarizes syntactically ... | JOINT RNN-BASED GREEDY PARSING AND WORD COMPOSITION |
d222378172 | Modern deep learning models have achieved great success in predictive accuracy for many data modalities. However, their application to many real-world tasks is restricted by poor uncertainty estimates, such as overconfidence on out-ofdistribution (OOD) data and ungraceful failing under distributional shift. Previous be... | EXPLORING THE UNCERTAINTY PROPERTIES OF NEU- RAL NETWORKS' IMPLICIT PRIORS IN THE INFINITE- WIDTH LIMIT |
d225040612 | Many problems across computer vision and the natural sciences require the analysis of spherical data, for which representations may be learned efficiently by encoding equivariance to rotational symmetries. We present a generalized spherical CNN framework that encompasses various existing approaches and allows them to b... | Published as a conference paper at ICLR 2021 EFFICIENT GENERALIZED SPHERICAL CNNS |
d13468104 | Current work in lexical distributed representations maps each word to a point vector in low-dimensional space. Mapping instead to a density provides many interesting advantages, including better capturing uncertainty about a representation and its relationships, expressing asymmetries more naturally than dot product or... | WORD REPRESENTATIONS VIA GAUSSIAN EMBEDDING |
d246823211 | We argue that a form of the valuable information provided by the auxiliary information is its implied data clustering information. For instance, considering hashtags as auxiliary information, we can hypothesize that an Instagram image will be semantically more similar with the same hashtags. With this intuition, we pre... | LEARNING WEAKLY-SUPERVISED CONTRASTIVE REP- RESENTATIONS |
d256901166 | Graph neural network (GNN) is a powerful learning approach for graph-based recommender systems. Recently, GNNs integrated with contrastive learning have shown superior performance in recommendation with their data augmentation schemes, aiming at dealing with highly sparse data. Despite their success, most existing grap... | LIGHTGCL: SIMPLE YET EFFECTIVE GRAPH CON- TRASTIVE LEARNING FOR RECOMMENDATION |
d238743879 | We give simpler, sparser, and faster algorithms for differentially private fine-tuning of large-scale pre-trained language models, which achieve the state-of-the-art privacy versus utility tradeoffs on many standard NLP tasks. We propose a meta-framework for this problem, inspired by the recent success of highly parame... | Differentially Private Fine-tuning of Language Models * |
d7454685 | 1arXiv:1511.03962v4[cs.CL]
Text documents are structured on multiple levels of detail: individual words are related by syntax, and larger units of text are related by discourse structure.Existing language models generally fail to account for discourse structure, but it is crucial if we are to have language models that ... | DOCUMENT CONTEXT LANGUAGE MODELS
21 Feb 2016
Yangfeng Ji |
d227240564 | The largely successful method of training neural networks is to learn their weights using some variant of stochastic gradient descent (SGD). Here, we show that the solutions found by SGD can be further improved by ensembling a subset of the weights in late stages of learning. At the end of learning, we obtain back a si... | Published as a conference paper at ICLR 2021 NEURAL NETWORKS WITH LATE-PHASE WEIGHTS |
d15461305 | Kernel density estimation, a.k.a. Parzen windows, is a popular density estimation method, which can be used for outlier detection or clustering. With multivariate data, its performance is heavily reliant on the metric used within the kernel. Most earlier work has focused on learning only the bandwidth of the kernel (i.... | Local Component Analysis |
d245704549 | In model-free deep reinforcement learning (RL) algorithms, using noisy value estimates to supervise policy evaluation and optimization is detrimental to the sample efficiency. As this noise is heteroscedastic, its effects can be mitigated using uncertainty-based weights in the optimization process. Previous methods rel... | SAMPLE EFFICIENT DEEP REINFORCEMENT LEARN- ING VIA UNCERTAINTY ESTIMATION |
d211532692 | Imitation Learning (IL) is a popular paradigm for training agents to achieve complicated goals by leveraging expert behavior, rather than dealing with the hardships of designing a correct reward function. With the environment modeled as a Markov Decision Process (MDP), most of the existing IL algorithms are contingent ... | STATE-ONLY IMITATION WITH TRANSITION DYNAM- ICS MISMATCH |
d231847376 | We build on the recently proposed EigenGame that views eigendecomposition as a competitive game. EigenGame's updates are biased if computed using minibatches of data, which hinders convergence and more sophisticated parallelism in the stochastic setting. In this work, we propose an unbiased stochastic update that is as... | EIGENGAME UNLOADED WHEN PLAYING GAMES IS BETTER THAN OPTIMIZING |
d252683779 | Conformal prediction is a distribution-free technique for establishing valid prediction intervals. Although conventionally people conduct conformal prediction in the output space, this is not the only possibility. In this paper, we propose feature conformal prediction, which extends the scope of conformal prediction to... | Published as a conference paper at ICLR 2023 PREDICTIVE INFERENCE WITH FEATURE CONFORMAL PREDICTION |
d251649129 | For supervised learning with tabular data, decision tree ensembles produced via boosting techniques generally dominate real-world applications involving iid training/test sets. However for graph data where the iid assumption is violated due to structured relations between samples, it remains unclear how to best incorpo... | Published as a conference paper at ICLR 2022 DOES YOUR GRAPH NEED A CONFIDENCE BOOST? CONVERGENT BOOSTED SMOOTHING ON GRAPHS WITH TABULAR NODE FEATURES |
d165163910 | Deep neural networks (DNNs) have great expressive power, which can even memorize samples with wrong labels. It is vitally important to reiterate robustness and generalization in DNNs against label corruption. To this end, this paper studies the 0-1 loss, which has a monotonic relationship with empirical adversary (rewe... | Published as a conference paper at ICLR 2020 CURRICULUM LOSS: ROBUST LEARNING AND GEN- ERALIZATION AGAINST LABEL CORRUPTION |
d251979354 | Deep reinforcement learning agents are notoriously sample inefficient, which considerably limits their application to real-world problems. Recently, many model-based methods have been designed to address this issue, with learning in the imagination of a world model being one of the most prominent approaches. However, w... | Published as a conference paper at ICLR 2023 TRANSFORMERS ARE SAMPLE-EFFICIENT WORLD MODELS |
d246706127 | Physics-inspired neural networks (NNs), such as Hamiltonian or Lagrangian NNs, dramatically outperform other learned dynamics models by leveraging strong inductive biases. These models, however, are challenging to apply to many real world systems, such as those that don't conserve energy or contain contacts, a common s... | Published as a conference paper at ICLR 2022 DECONSTRUCTING THE INDUCTIVE BIASES OF HAMILTONIAN NEURAL NETWORKS |
d246016131 | Class-conditioning offers a direct means to control a Generative Adversarial Network (GAN) based on a discrete input variable. While necessary in many applications, the additional information provided by the class labels could even be expected to benefit the training of the GAN itself. On the contrary, we observe that ... | Published as a conference paper at ICLR 2022 COLLAPSE BY CONDITIONING: TRAINING CLASS- CONDITIONAL GANS WITH LIMITED DATA |
d250526398 | Hypergraph neural networks (HNNs) using neural networks to encode hypergraphs provide a promising way to model higher-order relations in data and further solve relevant prediction tasks built upon such higher-order relations. However, higher-order relations in practice contain complex patterns and are often highly irre... | Published as a conference paper at ICLR 2023 EQUIVARIANT HYPERGRAPH DIFFUSION NEURAL OP- ERATORS |
d211133302 | The information bottleneck principle provides an information-theoretic method for representation learning, by training an encoder to retain all information which is relevant for predicting the label while minimizing the amount of other, excess information in the representation. The original formulation, however, requir... | Published as a conference paper at ICLR 2020 LEARNING ROBUST REPRESENTATIONS VIA MULTI-VIEW INFORMATION BOTTLENECK |
d216077591 | Differential equations are frequently used in engineering domains, such as modeling and control of industrial systems, where safety and performance guarantees are of paramount importance. Traditional physics-based modeling approaches require domain expertise and are often difficult to tune or adapt to new systems. In t... | Constrained Neural Ordinary Differential Equations with Stability Guarantees |
d247475806 | As reinforcement learning (RL) has achieved near human-level performance in a variety of tasks, its robustness has raised great attention. While a vast body of research has explored test-time (evasion) attacks in RL and corresponding defenses, its robustness against training-time (poisoning) attacks remains largely una... | COPA: CERTIFYING ROBUST POLICIES FOR OF- FLINE REINFORCEMENT LEARNING AGAINST POISON- ING ATTACKS |
d246652076 | Though remarkable progress has been achieved in various vision tasks, deep neural networks still suffer obvious performance degradation when tested in out-ofdistribution scenarios. We argue that the feature statistics (mean and standard deviation), which carry the domain characteristics of the training data, can be pro... | Published as a conference paper at ICLR 2022 UNCERTAINTY MODELING FOR OUT-OF-DISTRIBUTION GENERALIZATION |
d247362438 | Wide neural networks with linear output layer have been shown to be near-linear, and to have near-constant neural tangent kernel (NTK), in a region containing the optimization path of gradient descent. These findings seem counter-intuitive since in general neural networks are highly complex models. Why does a linear st... | TRANSITION TO LINEARITY OF WIDE NEURAL NET- WORKS IS AN EMERGING PROPERTY OF ASSEMBLING WEAK MODELS |
d211044132 | We propose a novel node embedding of directed graphs to statistical manifolds, which is based on a global minimization of pairwise relative entropy and graph geodesics in a non-linear way. Each node is encoded with a probability density function over a measurable space. Furthermore, we analyze the connection between th... | LOW-DIMENSIONAL STATISTICAL MANIFOLD EMBED- DING OF DIRECTED GRAPHS |
d251252882 | Recent large-scale text-driven synthesis models have attracted much attention thanks to their remarkable capabilities of generating highly diverse images that follow given text prompts. Such text-based synthesis methods are particularly appealing to humans who are used to verbally describe their intent. Therefore, it i... | Prompt-to-Prompt Image Editing with Cross Attention Control |
d786898 | Recently, image representation built upon Convolutional Neural Network (CNN) has been shown to provide effective descriptors for image search, outperforming pre-CNN features as short-vector representations. Yet such models are not compatible with geometry-aware re-ranking methods and still outperformed, on some parti... | PARTICULAR OBJECT RETRIEVAL WITH INTEGRAL MAX-POOLING OF CNN ACTIVATIONS |
d231985703 | Mixed-precision quantization can potentially achieve the optimal tradeoff between performance and compression rate of deep neural networks, and thus, have been widely investigated. However, it lacks a systematic method to determine the exact quantization scheme. Previous methods either examine only a small manuallydesi... | Published as a conference paper at ICLR 2021 BSQ: EXPLORING BIT-LEVEL SPARSITY FOR MIXED- PRECISION NEURAL NETWORK QUANTIZATION |
d210942708 | Recent research developing neural network architectures with external memory have often used the benchmark bAbI question and answering dataset which provides a challenging number of tasks requiring reasoning. Here we employed a classic associative inference task from the memory-based reasoning neuroscience literature i... | MEMO: A DEEP NETWORK FOR FLEXIBLE COMBINA- TION OF EPISODIC MEMORIES |
d211076166 | Identifying when to give treatments to patients and how to select among multiple treatments over time are important medical problems with a few existing solutions.In this paper, we introduce the Counterfactual Recurrent Network (CRN), a novel sequence-to-sequence model that leverages the increasingly available patient ... | |
d252683961 | Lighter and faster image restoration (IR) models are crucial for the deployment on resource-limited devices. Binary neural network (BNN), one of the most promising model compression methods, can dramatically reduce the computations and parameters of full-precision convolutional neural networks (CNN). However, there are... | Published as a conference paper at ICLR 2023 BASIC BINARY CONVOLUTION UNIT FOR BINARIZED IMAGE RESTORATION NETWORK |
d3284333 | Deep learning software demands reliability and performance. However, many of the existing deep learning frameworks are software libraries that act as an unsafe DSL in Python and a computation graph interpreter. We present DLVM, a design and implementation of a compiler infrastructure with a linear algebra intermediate ... | Workshop track -ICLR 2018 DLVM: A MODERN COMPILER INFRASTRUCTURE FOR DEEP LEARNING SYSTEMS |
d54447167 | We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundary. Our study shows that maximizing margins can be achieved by minimizing the adversarial loss on the decision boundary at the "shortest succ... | Published as a conference paper at ICLR 2020 MMA TRAINING: DIRECT INPUT SPACE MARGIN MAXIMIZATION THROUGH ADVERSARIAL TRAINING |
d231718715 | Federated learning (FL) is a distributed machine learning architecture that leverages a large number of workers to jointly learn a model with decentralized data. FL has received increasing attention in recent years thanks to its data privacy protection, communication efficiency and a linear speedup for convergence in t... | Published as a conference paper at ICLR 2021 ACHIEVING LINEAR SPEEDUP WITH PARTIAL WORKER PARTICIPATION IN NON-IID FEDERATED LEARNING |
d248006105 | Supervised federated learning (FL) enables multiple clients to share the trained model without sharing their labeled data. However, potential clients might even be reluctant to label their own data, which could limit the applicability of FL in practice. In this paper, we show the possibility of unsupervised FL whose mo... | Published as a conference paper at ICLR 2022 FEDERATED LEARNING FROM ONLY UNLABELED DATA WITH CLASS-CONDITIONAL-SHARING CLIENTS |
d257353246 | Many recent works have shown trainability plays a central role in neural network pruning -unattended broken trainability can lead to severe under-performance and unintentionally amplify the effect of retraining learning rate, resulting in biased (or even misinterpreted) benchmark results. This paper introduces trainabi... | Published as a conference paper at ICLR 2023 TRAINABILITY PRESERVING NEURAL PRUNING |
d4606753 | Synthesizing user-intended programs from a small number of input-output examples is a challenging problem with several important applications like spreadsheet manipulation, data wrangling and code refactoring. Existing synthesis systems either completely rely on deductive logic techniques that are extensively handengin... | NEURAL-GUIDED DEDUCTIVE SEARCH FOR REAL- TIME PROGRAM SYNTHESIS FROM EXAMPLES |
d221703133 | We study worst-case guarantees on the expected return of xed-dataset policy optimization algorithms. Our core contribution is a uni ed conceptual and mathematical framework for the study of algorithms in this regime.is analysis reveals that for naïve approaches, the possibility of erroneous value overestimation leads t... | e Importance of Pessimism in Fixed-Dataset Policy Optimization |
d6990380 | Figure 1: Our approach generates photorealistic output for various "incomplete" signals such as a low resolution image, a surface normal map, and edges/boundaries for human faces, cats, dogs, shoes, and handbags. Importantly, our approach can easily generate multiple outputs for a given input which was not possible in ... | PixelNN: Example-based Image Synthesis 12x12 Input (x8) Our Approach (a) Low-Resolution to High-Resolution Surface Normal Map Our Approach (b) Normals-to-RGB Edges Our Approach (c) Edges-to-RGB (d) Edges-to-RGB (Multiple Outputs) (e) Normals-to-RGB (Multiple Outputs) (d) Edges-to-Shoes (Multiple Outputs) (e) Edges-to-H... |
d252383259 | The separation between training and deployment of machine learning models implies that not all scenarios encountered in deployment can be anticipated during training, and therefore relying solely on advancements in training has its limits. Out-of-distribution (OOD) detection is an important area that stress-tests a mod... | Published as a conference paper at ICLR 2023 EXTREMELY SIMPLE ACTIVATION SHAPING FOR OUT- OF-DISTRIBUTION DETECTION |
d3433237 | It is widely believed that the success of deep convolutional networks is based on progressively discarding uninformative variability about the input with respect to the problem at hand. This is supported empirically by the difficulty of recovering images from their hidden representations, in most commonly used network ... | Published as a conference paper at ICLR 2018 i-REVNET: DEEP INVERTIBLE NETWORKS |
d258079386 | Modern image retrieval methods typically rely on fine-tuning pre-trained encoders to extract image-level descriptors. However, the most widely used models are pre-trained on ImageNet-1K with limited classes. The pre-trained feature representation is therefore not universal enough to generalize well to the diverse open-... | Published as a conference paper at ICLR 2023 UNICOM: UNIVERSAL AND COMPACT REPRESENTATION LEARNING FOR IMAGE RETRIEVAL |
d6017868 | To address memory and computation resource limitations for hardware-oriented acceleration of deep convolutional neural networks (CNNs), we present a computation flow, stacked filters stationary flow (SFS), and a corresponding data encoding format, relative indexed compressed sparse filter format (CSF), to make the best... | STACKED FILTERS STATIONARY FLOW FOR HARDWARE-ORIENTED ACCELERATION OF DEEP CONVOLUTIONAL NEURAL NETWORKS |
d255393759 | Many recent works on understanding deep learning try to quantify how much individual data instances influence the optimization and generalization of a model. Such attempts reveal characteristics and importance of individual instances, which may provide useful information in diagnosing and improving deep learning. Howev... | Published as a conference paper at ICLR 2023 DATA VALUATION WITHOUT TRAINING OF A MODEL |
d46935943 | Stochastic descent methods (of the gradient and mirror varieties) have become increasingly popular in optimization. In fact, it is now widely recognized that the success of deep learning is not only due to the special deep architecture of the models, but also due to the behavior of the stochastic descent methods used, ... | STOCHASTIC GRADIENT/MIRROR DESCENT: MINI- MAX OPTIMALITY AND IMPLICIT REGULARIZATION |
d257050266 | Dynamic diagnosis is desirable when medical tests are costly or time-consuming. In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test panels sequentially based on previous observations, ensuring accurate testing at a low cost. Clinical diagnostic data are often highly imbalance... | Published as a conference paper at ICLR 2023 DEEP REINFORCEMENT LEARNING FOR COST-EFFECTIVE MEDICAL DIAGNOSIS |
d244709251 | We propose Characteristic-Neural Ordinary Differential Equations (C-NODEs), a framework for extending Neural Ordinary Differential Equations (NODEs) beyond ODEs. While NODEs model the evolution of a latent variables as the solution to an ODE, C-NODE models the evolution of the latent variables as the solution of a fami... | Characteristic Neural Ordinary Differential Equations |
d13268684 | There has been a lot of prior work on representation learning for speech recognition applications, but not much emphasis has been given to an investigation of effective representations of affect from speech, where the paralinguistic elements of speech are separated out from the verbal content. In this paper, we explore... | Workshop track -ICLR 2016 LEARNING REPRESENTATIONS OF AFFECT FROM SPEECH |
d249209690 | The performance of deep neural networks is strongly influenced by the training dataset setup. In particular, when attributes with a strong correlation with the target attribute are present, the trained model can provide unintended prejudgments and show significant inference errors (i.e., the dataset bias problem). Vari... | Published as a conference paper at ICLR 2023 MITIGATING DATASET BIAS BY USING PER-SAMPLE GRADIENT |
d257365187 | The success of large-scale contrastive vision-language pretraining (CLIP) has benefited both visual recognition and multimodal content understanding. The concise design brings CLIP the advantage in inference efficiency against other visionlanguage models with heavier cross-attention fusion layers, making it a popular c... | Published as a conference paper at ICLR 2023 HICLIP: CONTRASTIVE LANGUAGE-IMAGE PRE- TRAINING WITH HIERARCHY-AWARE ATTENTION |
d250072832 | Content creators compete for user attention. Their reach crucially depends on algorithmic choices made by developers on online platforms. To maximize exposure, many creators adapt strategically, as evidenced by examples like the sprawling search engine optimization industry. This begets competition for the finite user ... | Published as a conference paper at ICLR 2023 MODELING CONTENT CREATOR INCENTIVES ON ALGORITHM-CURATED PLATFORMS |
d195820512 | Adversarial examples raise questions about whether neural network models are sensitive to the same visual features as humans. In this paper, we first detect adversarial examples or otherwise corrupted images based on a class-conditional reconstruction of the input. To specifically attack our detection mechanism, we pro... | Published as a conference paper at ICLR 2020 DETECTING AND DIAGNOSING ADVERSARIAL IMAGES WITH CLASS-CONDITIONAL CAPSULE RECONSTRUCTIONS |
d257232577 | We address the challenging problem of jointly inferring the 3D flow and volumetric densities moving in a fluid from a monocular input video with a deep neural network. Despite the complexity of this task, we show that it is possible to train the corresponding networks without requiring any 3D ground truth for training.... | Published as a conference paper at ICLR 2023 LEARNING TO ESTIMATE SINGLE-VIEW VOLUMETRIC FLOW MOTIONS WITHOUT 3D SUPERVISION |
d235613377 | Despite the success of recent Neural Architecture Search (NAS) methods on various tasks which have shown to output networks that largely outperform humandesigned networks, conventional NAS methods have mostly tackled the optimization of searching for the network architecture for a single task (dataset), which does not ... | Published as a conference paper at ICLR 2021 RAPID NEURAL ARCHITECTURE SEARCH BY LEARNING TO GENERATE GRAPHS FROM DATASETS |
d235368380 | The empirical success of deep convolutional networks on tasks involving highdimensional data such as images or audio suggests that they can efficiently approximate certain functions that are well-suited for such tasks. In this paper, we study this through the lens of kernel methods, by considering simple hierarchical k... | Published as a conference paper at ICLR 2022 APPROXIMATION AND LEARNING WITH DEEP CONVOLUTIONAL MODELS: A KERNEL PERSPECTIVE |
d247011143 | Recent studies demonstrate that deep networks, even robustified by the state-ofthe-art adversarial training (AT), still suffer from large robust generalization gaps, in addition to the much more expensive training costs than standard training. In this paper, we investigate this intriguing problem from a new perspective... | Published as a conference paper at ICLR 2022 SPARSITY WINNING TWICE: BETTER ROBUST GEN- ERALIZATION FROM MORE EFFICIENT TRAINING |
d6530726 | We study the problem of building models that disentangle independent factors of variation. Such models could be used to encode features that can efficiently be used for classification and to transfer attributes between different images in image synthesis. As data we use a weakly labeled training set. Our weak labels in... | CHALLENGES IN DISENTANGLING INDEPENDENT FAC- TORS OF VARIATION * |
d251648016 | Spiking Neural Networks (SNNs) have gained great attraction due to their distinctive properties of low power consumption and fast inference on neuromorphic hardware. As the most effective method to get deep SNNs, ANN-SNN conversion has achieved comparable performance as ANNs on large-scale datasets. Despite this, it re... | OPTIMAL ANN-SNN CONVERSION FOR HIGH- ACCURACY AND ULTRA-LOW-LATENCY SPIKING NEURAL NETWORKS |
d8208459 | Reinforcement learning is concerned with identifying reward-maximizing behaviour policies in environments that are initially unknown. Stateof-the-art reinforcement learning approaches, such as deep Q-networks, are model-free and learn to act effectively across a wide range of environments such as Atari games, but requi... | A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games |
d53015027 | Deep learning has shown high performances in various types of tasks from visual recognition to natural language processing, which indicates superior flexibility and adaptivity of deep learning. To understand this phenomenon theoretically, we develop a new approximation and estimation error analysis of deep learning wit... | ADAPTIVITY OF DEEP RELU NETWORK FOR LEARN- ING IN BESOV AND MIXED SMOOTH BESOV SPACES: OPTIMAL RATE AND CURSE OF DIMENSIONALITY |
d256459906 | Diffusion models generate samples by reversing a fixed forward diffusion process. Despite already providing impressive empirical results, these diffusion models algorithms can be further improved by reducing the variance of the training targets in their denoising score-matching objective. We argue that the source of su... | Published as a conference paper at ICLR 2023 STABLE TARGET FIELD FOR REDUCED VARIANCE SCORE ESTIMATION IN DIFFUSION MODELS |
d257766694 | Dense prediction tasks are a fundamental class of problems in computer vision. As supervised methods suffer from high pixel-wise labeling cost, a few-shot learning solution that can learn any dense task from a few labeled images is desired. Yet, current few-shot learning methods target a restricted set of tasks such as... | UNIVERSAL FEW-SHOT LEARNING OF DENSE PREDIC- TION TASKS WITH VISUAL TOKEN MATCHING |
d212725580 | Adversarial patch attacks are among of the most practical threat models against realworld computer vision systems. This paper studies certified and empirical defenses against patch attacks. We begin with a set of experiments showing that most existing defenses, which work by pre-processing input images to mitigate adve... | Published as a conference paper at ICLR 2020 CERTIFIED DEFENSES FOR ADVERSARIAL PATCHES * |
d203591519 | Despite alarm over the reliance of machine learning systems on so-called spurious patterns, the term lacks coherent meaning in standard statistical frameworks. However, the language of causality offers clarity: spurious associations are due to confounding (e.g., a common cause), but not direct or indirect causal effect... | Published as a conference paper at ICLR 2020 LEARNING THE DIFFERENCE THAT MAKES A DIFFER- ENCE WITH COUNTERFACTUALLY-AUGMENTED DATA |
d252683397 | Reward and representation learning are two long-standing challenges for learning an expanding set of robot manipulation skills from sensory observations. Given the inherent cost and scarcity of in-domain, task-specific robot data, learning from large, diverse, offline human videos has emerged as a promising path toward... | Published as a conference paper at ICLR 2023 TOWARDS UNIVERSAL VISUAL REWARD AND REPRE- SENTATION VIA VALUE-IMPLICIT PRE-TRAINING |
d14079971 | Recent studies have shown that deep neural networks (DNN) are vulnerable to adversarial samples: maliciously-perturbed samples crafted to yield incorrect model outputs. Such attacks can severely undermine DNN systems, particularly in security-sensitive settings. It was observed that an adversary could easily generate a... | Workshop track -ICLR 2017 DEEPCLOAK: MASKING DNN MODELS FOR ROBUST- NESS AGAINST ADVERSARIAL SAMPLES |
d19326248 | Genetic algorithms have been widely used in many practical optimization problems. Inspired by natural selection, operators, including mutation, crossover and selection, provide effective heuristics for search and black-box optimization. However, they have not been shown useful for deep reinforcement learning, possibly ... | GENETIC POLICY OPTIMIZATION |
d254591489 | We present RAVEn, a self-supervised multi-modal approach to jointly learn visual and auditory speech representations. Our pre-training objective involves encoding masked inputs, and then predicting contextualised targets generated by slowly-evolving momentum encoders. Driven by the inherent differences between video an... | Published as a conference paper at ICLR 2023 JOINTLY LEARNING VISUAL AND AUDITORY SPEECH REPRESENTATIONS FROM RAW DATA |
d244117525 | Unsupervised large-scale vision-language pre-training has shown promising advances on various downstream tasks. Existing methods often model the crossmodal interaction either via the similarity of the global feature of each modality which misses sufficient information, or finer-grained interactions using cross/selfatte... | FILIP: FINE-GRAINED INTERACTIVE LANGUAGE- IMAGE PRE-TRAINING |
d13747555 | Deep residual networks (ResNets) made a recent breakthrough in deep learning. The core idea of ResNets is to have shortcut connections between layers that allow the network to be much deeper while still being easy to optimize avoiding vanishing gradients. These shortcut connections have interesting side-effects that ma... | Workshop track -ICLR 2018 IAMNN: ITERATIVE AND ADAPTIVE MOBILE NEURAL NETWORK FOR EFFICIENT IMAGE CLASSIFICATION |
d248524779 | Differentiable physics has recently been shown as a powerful tool for solving soft-body manipulation tasks. However, the differentiable physics solver often gets stuck when the initial contact points of the end effectors are sub-optimal or when performing multi-stage tasks that require contact point switching, which of... | Published as a conference paper at ICLR 2022 CONTACT POINTS DISCOVERY FOR SOFT-BODY MA- NIPULATIONS WITH DIFFERENTIABLE PHYSICS |
d24044364 | Recently, the problem of local minima in very high dimensional non-convex optimization has been challenged and the problem of saddle points has been introduced. This paper introduces a dynamic type of normalization that forces the system to escape saddle points. Unlike other saddle point escaping algorithms, second ord... | Workshop track -ICLR 2017 CHARGED POINT NORMALIZATION AN EFFICIENT SOLUTION TO THE SADDLE POINT PROBLEM |
d247839322 | We present a meta-learning framework for learning new visual concepts quickly, from just one or a few examples, guided by multiple naturally occurring data streams: simultaneously looking at images, reading sentences that describe the objects in the scene, and interpreting supplemental sentences that relate the novel c... | Published as a conference paper at ICLR 2022 FALCON: FAST VISUAL CONCEPT LEARNING BY IN- TEGRATING IMAGES, LINGUISTIC DESCRIPTIONS, AND CONCEPTUAL RELATIONS |
d2753399 | For most deep learning algorithms training is notoriously time consuming. Since most of the computation in training neural networks is typically spent on floating point multiplications, we investigate an approach to training that eliminates the need for most of these. Our method consists of two parts: First we stochast... | Published as a conference paper at ICLR 2016 NEURAL NETWORKS WITH FEW MULTIPLICATIONS |
d257771315 | Recent works have shown that sequence modeling can be effectively used to train reinforcement learning (RL) policies. However, the success of applying existing sequence models to planning, in which we wish to obtain a trajectory of actions to reach some goal, is less straightforward. The typical autoregressive generati... | Published as a conference paper at ICLR 2023 PLANNING WITH SEQUENCE MODELS THROUGH ITER- ATIVE ENERGY MINIMIZATION |
d251280143 | In this paper, we study how to use masked signal modeling in vision and language (V+L) representation learning. Instead of developing masked language modeling (MLM) and masked image modeling (MIM) independently, we propose to build joint masked vision and language modeling, where the masked signal of one modality is re... | Published as a conference paper at ICLR 2023 MASKED VISION AND LANGUAGE MODELING FOR MULTI-MODAL REPRESENTATION LEARNING |
d231740588 | Knowledge distillation is an effective approach to leverage a well-trained network or an ensemble of them, named as the teacher, to guide the training of a student network. The outputs from the teacher network are used as soft labels for supervising the training of a new network. Recent studies(Müller et al., 2019;Yuan... | Published as a conference paper at ICLR 2021 RETHINKING SOFT LABELS FOR KNOWLEDGE DISTIL- LATION: A BIAS-VARIANCE TRADEOFF PERSPECTIVE |
d2974620 | We present a representation learning method that learns features at multiple different levels of scale. Working within the unsupervised framework of denoising autoencoders, we observe that when the input is heavily corrupted during training, the network tends to learn coarse-grained features, whereas when the input is ... | Scheduled denoising autoencoders |
d232147821 | We propose a simple, general and effective technique, Reward Randomization for discovering diverse strategic policies in complex multi-agent games. Combining reward randomization and policy gradient, we derive a new algorithm, Reward-Randomized Policy Gradient (RPG). RPG is able to discover multiple distinctive human-i... | Published as a conference paper at ICLR 2021 DISCOVERING DIVERSE MULTI-AGENT STRATEGIC BEHAVIOR VIA REWARD RANDOMIZATION |
d26945723 | Neural networks have recently had a lot of success for many tasks. However, neural network architectures that perform well are still typically designed manually by experts in a cumbersome trial-and-error process. We propose a new method to automatically search for well-performing CNN architectures based on a simple hil... | Under review as a conference paper at ICLR 2018 SIMPLE AND EFFICIENT ARCHITECTURE SEARCH FOR CONVOLUTIONAL NEURAL NETWORKS |
d210064459 | Deep neural networks achieve remarkable performance in many computer vision tasks. Most state-of-the-art (SOTA) semantic segmentation and object detection approaches reuse neural network architectures designed for image classification as the backbone, commonly pre-trained on ImageNet. However, performance gains can be ... | Published as a conference paper at ICLR 2020 FAST NEURAL NETWORK ADAPTATION VIA PARAME- TER REMAPPING AND ARCHITECTURE SEARCH |
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