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d3580738 | Deep learning methods have achieved high performance in sound recognition tasks. Deciding how to feed the training data is important for further performance improvement. We propose a novel learning method for deep sound recognition: Between-Class learning (BC learning). Our strategy is to learn a discriminative feature... | Under review as a conference paper at ICLR 2018 LEARNING FROM BETWEEN-CLASS EXAMPLES FOR DEEP SOUND RECOGNITION |
d44132187 | Regularized Nonlinear Acceleration (RNA) can improve the rate of convergence of many optimization schemes such as gradient descent, SAGA or SVRG, estimating the optimum using a nonlinear average of past iterates. Until now, its analysis was limited to convex problems, but empirical observations show that RNA may be ext... | NONLINEAR ACCELERATION OF CNNS |
d252683180 | Training graph neural networks (GNNs) on large graphs is complex and extremely time consuming. This is attributed to overheads caused by sparse matrix multiplication, which are sidestepped when training multi-layer perceptrons (MLPs) with only node features. MLPs, by ignoring graph context, are simple and faster for gr... | MLPINIT: EMBARRASSINGLY SIMPLE GNN TRAIN- ING ACCELERATION WITH MLP INITIALIZATION |
d257050828 | Recently, great success has been made in learning visual representations from text supervision, facilitating the emergence of text-supervised semantic segmentation. However, existing works focus on pixel grouping and cross-modal semantic alignment, while ignoring the correspondence among multiple augmented views of the... | VIEWCO: DISCOVERING TEXT-SUPERVISED SEGMEN- TATION MASKS VIA MULTI-VIEW SEMANTIC CONSIS- TENCY |
d258426317 | Recently, transformers have shown strong ability as visual feature extractors, surpassing traditional convolution-based models in various scenarios. However, the success of vision transformers largely owes to their capacity to accommodate numerous parameters. As a result, new challenges for adapting a well-trained tran... | CONSOLIDATOR: MERGEABLE ADAPTER WITH GROUPED CONNECTIONS FOR VISUAL ADAPTATION |
d534043 | A critical component to enabling intelligent reasoning in partially observable environments is memory. Despite this importance, Deep Reinforcement Learning (DRL) agents have so far used relatively simple memory architectures, with the main methods to overcome partial observability being either a temporal convolution ov... | NEURAL MAP: STRUCTURED MEMORY FOR DEEP RE- INFORCEMENT LEARNING |
d257427351 | Despite their success with unstructured data, deep neural networks are not yet a panacea for structured tabular data. In the tabular domain, their efficiency crucially relies on various forms of regularization to prevent overfitting and provide strong generalization performance. Existing regularization techniques inclu... | TANGOS: REGULARIZING TABULAR NEURAL NET- WORKS THROUGH GRADIENT ORTHOGONALIZATION AND SPECIALIZATION |
d406912 | Humans can understand and produce new utterances effortlessly, thanks to their systematic compositional skills. Once a person learns the meaning of a new verb "dax," he or she can immediately understand the meaning of "dax twice" or "sing and dax." In this paper, we introduce the SCAN domain, consisting of a set of sim... | Under review STILL NOT SYSTEMATIC AFTER ALL THESE YEARS: ON THE COMPOSITIONAL SKILLS OF SEQUENCE-TO- SEQUENCE RECURRENT NETWORKS |
d235613622 | Differentiable Neural Architecture Search is one of the most popular Neural Architecture Search (NAS) methods for its search efficiency and simplicity, accomplished by jointly optimizing the model weight and architecture parameters in a weight-sharing supernet via gradient-based algorithms. At the end of the search pha... | Published as a conference paper at ICLR 2021 RETHINKING ARCHITECTURE SELECTION IN DIFFER- ENTIABLE NAS |
d44167055 | The problem of attributing a deep network's prediction to its input/base features is well-studied (cf.[1]). We introduce the notion of conductance to extend the notion of attribution to the understanding the importance of hidden units. Informally, the conductance of a hidden unit of a deep network is the flow of attrib... | How Important Is a Neuron? |
d168169556 | Disentangled representations have recently been shown to improve fairness, data efficiency and generalisation in simple supervised and reinforcement learning tasks. To extend the benefits of disentangled representations to more complex domains and practical applications, it is important to enable hyperparameter tuning ... | Published as a conference paper at ICLR 2020 UNSUPERVISED MODEL SELECTION FOR VARIATIONAL DISENTANGLED REPRESENTATION LEARNING |
d1840346 | We present LR-GAN: an adversarial image generation model which takes scene structure and context into account. Unlike previous generative adversarial networks (GANs), the proposed GAN learns to generate image background and foregrounds separately and recursively, and stitch the foregrounds on the background in a contex... | LR-GAN: LAYERED RECURSIVE GENERATIVE AD- VERSARIAL NETWORKS FOR IMAGE GENERATION |
d252519173 | Much recent research on information retrieval has focused on how to transfer from one task (typically with abundant supervised data) to various other tasks where supervision is limited, with the implicit assumption that it is possible to generalize from one task to all the rest. However, this overlooks the fact that th... | PROMPTAGATOR : FEW-SHOT DENSE RETRIEVAL FROM 8 EXAMPLES |
d13739955 | Many recently trained neural networks employ large numbers of parameters to achieve good performance. One may intuitively use the number of parameters required as a rough gauge of the difficulty of a problem. But how accurate are such notions? How many parameters are really needed? In this paper we attempt to answer th... | Published as a conference paper at ICLR 2018 MEASURING THE INTRINSIC DIMENSION OF OBJECTIVE LANDSCAPES |
d248665921 | A plethora of machine learning methods have been applied to imaging data, enabling the construction of clinically relevant imaging signatures of neurological and neuropsychiatric diseases. Oftentimes, such methods do not explicitly model the heterogeneity of disease effects, or approach it via nonlinear models that are... | SURREAL-GAN:SEMI-SUPERVISED REPRESENTATION LEARNING VIA GAN FOR UNCOVERING HETEROGE- NEOUS DISEASE-RELATED IMAGING PATTERNS |
d208139568 | We present a new method for black-box adversarial attack. Unlike previous methods that combined transfer-based and scored-based methods by using the gradient or initialization of a surrogate white-box model, this new method tries to learn a low-dimensional embedding using a pretrained model, and then performs efficient... | BLACK-BOX ADVERSARIAL ATTACK WITH TRANS- FERABLE MODEL-BASED EMBEDDING |
d219793033 | While second order optimizers such as natural gradient descent (NGD) often speed up optimization, their effect on generalization remains controversial. For instance, it has been pointed out that gradient descent (GD), in contrast to many preconditioned updates, converges to small Euclidean norm solutions in overparamet... | When Does Preconditioning Help or Hurt Generalization? * |
d57375742 | This work presents a new strategy for multi-class classification that requires no class-specific labels, but instead leverages pairwise similarity between examples, which is a weaker form of annotation. The proposed method, meta classification learning, optimizes a binary classifier for pairwise similarity prediction a... | MULTI-CLASS CLASSIFICATION WITHOUT MULTI- CLASS LABELS |
d210713989 | In this work, we address semi-supervised classification of graph data, where the categories of those unlabeled nodes are inferred from labeled nodes as well as graph structures. Recent works often solve this problem via advanced graph convolution in a conventionally supervised manner, but the performance could degrade ... | Published as a conference paper at ICLR 2020 GRAPH INFERENCE LEARNING FOR SEMI-SUPERVISED CLASSIFICATION |
d247447155 | Recent studies on Learning to Optimize (L2O) suggest a promising path to automating and accelerating the optimization procedure for complicated tasks. Existing L2O models parameterize optimization rules by neural networks, and learn those numerical rules via meta-training. However, they face two common pitfalls: (1) sc... | Published as a conference paper at ICLR 2022 SYMBOLIC LEARNING TO OPTIMIZE: TOWARDS IN- TERPRETABILITY AND SCALABILITY |
d9398766 | We propose local distributional smoothness (LDS), a new notion of smoothness for statistical model that can be used as a regularization term to promote the smoothness of the model distribution. We named the LDS based regularization as virtual adversarial training (VAT). The LDS of a model at an input datapoint is defin... | DISTRIBUTIONAL SMOOTHING WITH VIRTUAL ADVERSARIAL TRAINING |
d203593798 | We study non-collaborative dialogs, where two agents have a conflict of interest but must strategically communicate to reach an agreement (e.g., negotiation). This setting poses new challenges for modeling dialog history because the dialog's outcome relies not only on the semantic intent, but also on tactics that conve... | AUGMENTING NON-COLLABORATIVE DIALOG SYS- TEMS WITH EXPLICIT SEMANTIC AND STRATEGIC DI- ALOG HISTORY |
d239016251 | Learning from set-structured data is a fundamental problem that has recently attracted increasing attention, where a series of summary networks are introduced to deal with the set input. In fact, many meta-learning problems can be treated as set-input tasks. Most existing summary networks aim to design different archit... | LEARNING PROTOTYPE-ORIENTED SET REPRESENTA- TIONS FOR META-LEARNING |
d255416037 | The approximate nearest neighbor (ANN) search problem is fundamental to efficiently serving many real-world machine learning applications. A number of techniques have been developed for ANN search that are efficient, accurate, and scalable. However, such techniques typically have a number of parameters that affect the ... | AUTOMATING NEAREST NEIGHBOR SEARCH CONFIG- URATION WITH CONSTRAINED OPTIMIZATION |
d237416585 | This paper explores a simple method for improving the zero-shot learning abilities of language models. We show that instruction tuning-finetuning language models on a collection of datasets described via instructions-substantially improves zeroshot performance on unseen tasks. | Published as a conference paper at ICLR 2022 FINETUNED LANGUAGE MODELS ARE ZERO-SHOT LEARNERS |
d222133140 | In adversarial machine learning, there was a common belief that robustness and accuracy hurt each other. The belief was challenged by recent studies where we can maintain the robustness and improve the accuracy. However, the other direction, we can keep the accuracy and improve the robustness, is conceptually and pract... | Published as a conference paper at ICLR 2021 GEOMETRY-AWARE INSTANCE-REWEIGHTED ADVER- SARIAL TRAINING |
d256598148 | This paper presents a novel end-to-end framework with Explicit box Detection for multi-person Pose estimation, called ED-Pose, where it unifies the contextual learning between human-level (global) and keypoint-level (local) information. Different from previous one-stage methods, ED-Pose re-considers this task as two ex... | Published as a conference paper at ICLR 2023 EXPLICIT BOX DETECTION UNIFIES END-TO-END MULTI-PERSON POSE ESTIMATION |
d231786358 | Wasserstein barycenters provide a geometric notion of the weighted average of probability measures based on optimal transport. In this paper, we present a scalable algorithm to compute Wasserstein-2 barycenters given sample access to the input measures, which are not restricted to being discrete. While past approaches ... | arXiv pre-print CONTINUOUS WASSERSTEIN-2 BARYCENTER ESTIMATION WITHOUT MINIMAX OPTIMIZATION |
d61153651 | AWe study the interplay between memorization and generalization of overparameterized networks in the extreme case of a single training example and an identity-mapping task. We examine fully-connected and convolutional networks (FCN and CNN), both linear and nonlinear, initialized randomly and then trained to minimize t... | Published as a conference paper at ICLR 2020 I C : M G E O |
d257364778 | Open-ended learning methods that automatically generate a curriculum of increasingly challenging tasks serve as a promising avenue toward generally capable reinforcement learning agents. Existing methods adapt curricula independently over either environment parameters (in single-agent settings) or co-player policies (i... | Published as a conference paper at ICLR 2023 MAESTRO: OPEN-ENDED ENVIRONMENT DESIGN FOR MULTI-AGENT REINFORCEMENT LEARNING |
d238420480 | The importance of Variational Autoencoders reaches far beyond standalone generative models -the approach is also used for learning latent representations and can be generalized to semi-supervised learning.This requires a thorough analysis of their commonly known shortcomings: posterior collapse and approximation errors... | |
d207847275 | Non-autoregressive machine translation (NAT) systems predict a sequence of output tokens in parallel, achieving substantial improvements in generation speed compared to autoregressive models. Existing NAT models usually rely on the technique of knowledge distillation, which creates the training data from a pretrained a... | Preprint. Under review UNDERSTANDING KNOWLEDGE DISTILLATION IN NON-AUTOREGRESSIVE MACHINE TRANSLATION |
d80628419 | Despite impressive performance as evaluated on i.i.d. holdout data, deep neural networks depend heavily on superficial statistics of the training data and are liable to break under distribution shift. For example, subtle changes to the background or texture of an image can break a seemingly powerful classifier. Buildin... | LEARNING ROBUST REPRESENTATIONS BY PROJECT- ING SUPERFICIAL STATISTICS OUT |
d256627643 | Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries embed a hidden backdoor trigger during the training process for malicious prediction manipulation. These attacks pose great threats to the applications of DNNs under the real-world machine learning as a service (MLaaS) setting, where the ... | SCALE-UP: AN EFFICIENT BLACK-BOX INPUT- LEVEL BACKDOOR DETECTION VIA ANALYZING SCALED PREDICTION CONSISTENCY |
d195750622 | Data selection methods such as active learning and core-set selection are useful tools for machine learning on large datasets, but they can be prohibitively expensive to apply in deep learning. Unlike in other areas of machine learning, the feature representations that these techniques depend on are learned in deep lea... | Selection Via Proxy: Efficient Data Selection For Deep Learning * |
d256808572 | This paper proposes a novel batch normalization strategy for test-time adaptation. Recent test-time adaptation methods heavily rely on the modified batch normalization, i.e., transductive batch normalization (TBN), which calculates the mean and the variance from the current test batch rather than using the running mean... | Published as a conference paper at ICLR 2023 TTN: A DOMAIN-SHIFT AWARE BATCH NORMALIZA- TION IN TEST-TIME ADAPTATION |
d17707860 | An intriguing property of deep neural networks is the existence of adversarial examples, which can transfer among different architectures. These transferable adversarial examples may severely hinder deep neural network-based applications. Previous works mostly study the transferability using small scale datasets. In th... | Published as a conference paper at ICLR 2017 DELVING INTO TRANSFERABLE ADVERSARIAL EX- AMPLES AND BLACK-BOX ATTACKS |
d258048386 | We present a method to map 2D image observations of a scene to a persistent 3D scene representation, enabling novel view synthesis and disentangled representation of the movable and immovable components of the scene. Motivated by the bird's-eye-view (BEV) representation commonly used in vision and robotics, we propose ... | Published as a conference paper at ICLR 2023 NEURAL GROUNDPLANS: PERSISTENT NEURAL SCENE REPRESENTATIONS FROM A SINGLE IMAGE |
d200884 | Many machine learning systems are built to solve the hardest examples of a particular task, which often makes them large and expensive to run-especially with respect to the easier examples, which might require much less computation. For an agent with a limited computational budget, this "one-size-fits-all" approach may... | Published as a conference paper at ICLR 2017 METACONTROL FOR ADAPTIVE IMAGINATION-BASED OPTIMIZATION |
d253734705 | Electron cryo-microscopy (cryo-EM) produces three-dimensional (3D) maps of the electrostatic potential of biological macromolecules, including proteins. Along with knowledge about the imaged molecules, cryo-EM maps allow de novo atomic modeling, which is typically done through a laborious manual process. Taking inspira... | A GRAPH NEURAL NETWORK APPROACH TO AUTO- MATED MODEL BUILDING IN CRYO-EM MAPS |
d249097738 | Nonlinear dynamics is ubiquitous in nature and commonly seen in various science and engineering disciplines. Distilling analytical expressions that govern nonlinear dynamics from limited data remains vital but challenging. To tackle this fundamental issue, we propose a novel Symbolic Physics Learner (SPL) machine to di... | Published as a conference paper at ICLR 2023 SYMBOLIC PHYSICS LEARNER: DISCOVERING GOV- ERNING EQUATIONS VIA MONTE CARLO TREE SEARCH |
d249191700 | Quantile regression (QR) is a powerful tool for estimating one or more conditional quantiles of a target variable Y given explanatory features X. A limitation of QR is that it is only defined for scalar target variables, due to the formulation of its objective function, and since the notion of quantiles has no standard... | Published as a conference paper at ICLR 2023 FAST NONLINEAR VECTOR QUANTILE REGRESSION |
d159041190 | Transfer learning, in which a network is trained on one task and re-purposed on another, is often used to produce neural network classifiers when data is scarce or full-scale training is too costly. When the goal is to produce a model that is not only accurate but also adversarially robust, data scarcity and computatio... | Published as a conference paper at ICLR 2020 ADVERSARIALLY ROBUST TRANSFER LEARNING |
d14715110 | Recurrent neural network is a powerful model that learns temporal patterns in sequential data. For a long time, it was believed that recurrent networks are difficult to train using simple optimizers, such as stochastic gradient descent, due to the so-called vanishing gradient problem. In this paper, we show that learni... | LEARNING LONGER MEMORY IN RECURRENT NEURAL NETWORKS |
d5273326 | We introduce the "exponential linear unit" (ELU) which speeds up learning in deep neural networks and leads to higher classification accuracies. Like rectified linear units (ReLUs), leaky ReLUs (LReLUs) and parametrized ReLUs (PRe-LUs), ELUs alleviate the vanishing gradient problem via the identity for positive values.... | Published as a conference paper at ICLR 2016 FAST AND ACCURATE DEEP NETWORK LEARNING BY EXPONENTIAL LINEAR UNITS (ELUS) |
d14426518 | We introduce a simple new regularizer for auto-encoders whose hidden-unit activation functions contain at least one zero-gradient (saturated) region. This regularizer explicitly encourages activations in the saturated region(s) of the corresponding activation function. We call these Saturating Auto-Encoders (SATAE). We... | Saturating Auto-Encoders |
d10052258 | PLAYING SNES IN THE RETRO LEARNING ENVIRONMENT | |
d252715683 | Optimizing multiple competing objectives is a common problem across science and industry.The inherent inextricable trade-off between those objectives leads one to the task of exploring their Pareto front.A meaningful quantity for the purpose of the latter is the hypervolume indicator, which is used in Bayesian Optimiza... | |
d231592887 | The two main impediments to continual learning are catastrophic forgetting and memory limitations on the storage of data. To cope with these challenges, we propose a novel, cognitively-inspired approach which trains autoencoders with Neural Style Transfer to encode and store images. During training on a new task, recon... | EEC: LEARNING TO ENCODE AND REGENERATE IM- AGES FOR CONTINUAL LEARNING |
d256868505 | Quasar convexity is a condition that allows some first-order methods to efficiently minimize a function even when the optimization landscape is non-convex. Previous works develop near-optimal accelerated algorithms for minimizing this class of functions, however, they require a subroutine of binary search which results... | Published as a conference paper at ICLR 2023 CONTINUIZED ACCELERATION FOR QUASAR CONVEX FUNCTIONS IN NON-CONVEX OPTIMIZATION |
d251648657 | We investigate whether three types of post hoc model explanations-feature attribution, concept activation, and training point ranking-are effective for detecting a model's reliance on spurious signals in the training data. Specifically, we consider the scenario where the spurious signal to be detected is unknown, at te... | Published as a conference paper at ICLR 2022 POST HOC EXPLANATIONS MAY BE INEFFECTIVE FOR DETECTING UNKNOWN SPURIOUS CORRELATION |
d232146784 | Episodic and semantic memory are critical components of the human memory model. The theory of complementary learning systems(McClelland et al., 1995)suggests that the compressed representation produced by a serial event (episodic memory) is later restructured to build a more generalized form of reusable knowledge (sema... | Published as a conference paper at ICLR 2021 KANERVA++: EXTENDING THE KANERVA MACHINE WITH DIFFERENTIABLE, LOCALLY BLOCK ALLOCATED LATENT MEMORY |
d4071727 | We present an integrated framework for using Convolutional Networks for classification, localization and detection. We show how a multiscale and sliding window approach can be efficiently implemented within a ConvNet. We also introduce a novel deep learning approach to localization by learning to predict object boundar... | OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks |
d252815945 | Humans are remarkably good at understanding and reasoning about complex visual scenes. The capability to decompose low-level observations into discrete objects allows us to build a grounded abstract representation and identify the compositional structure of the world. Accordingly, it is a crucial step for machine learn... | Robust and Controllable Object-Centric Learning through Energy-based Models ROBUST AND CONTROLLABLE OBJECT-CENTRIC LEARNING THROUGH ENERGY-BASED MODELS |
d86554567 | Deep Neural Networks (DNNs) are increasingly deployed in highly energyconstrained environments such as autonomous drones and wearable devices while at the same time must operate in real-time. Therefore, reducing the energy consumption has become a major design consideration in DNN training. This paper proposes the firs... | ENERGY-CONSTRAINED COMPRESSION FOR DEEP NEURAL NETWORKS VIA WEIGHTED SPARSE PROJEC- TION AND LAYER INPUT MASKING |
d259298658 | Constraint satisfaction problems (CSPs) are about finding values of variables that satisfy the given constraints. We show that Transformer extended with recurrence is a viable approach to learning to solve CSPs in an end-to-end manner, having clear advantages over state-of-the-art methods such as Graph Neural Networks,... | Published as a conference paper at ICLR 2023 LEARNING TO SOLVE CONSTRAINT SATISFACTION PROBLEMS WITH RECURRENT TRANSFORMER |
d211259427 | Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images)(Tsipras et al., 2019). Such a dilemma is shown to be rooted in the inherently higher sample complexity ) and/or model capacity (Nakkiran, 2019), for learning a high-accura... | Published as a conference paper at ICLR 2020 TRIPLE WINS: BOOSTING ACCURACY, ROBUSTNESS AND EFFICIENCY TOGETHER BY ENABLING INPUT- ADAPTIVE INFERENCE |
d208527060 | Recent advances in the sparse neural network literature have made it possible to prune many large feed forward and convolutional networks with only a small quantity of data. Yet, these same techniques often falter when applied to the problem of recovering sparse recurrent networks. These failures are quantitative: when... | ONE-SHOT PRUNING OF RECURRENT NEURAL NET- WORKS BY JACOBIAN SPECTRUM EVALUATION |
d12268909 | Neural language models learn word representations, or embeddings, that capture rich linguistic and conceptual information. Here we investigate the embeddings learned by neural machine translation models, a recently-developed class of neural language model. We show that embeddings from translation models outperform thos... | EMBEDDING WORD SIMILARITY WITH NEURAL MACHINE TRANSLATION |
d257557257 | Contrastive learning is a cornerstone underlying recent progress in multi-view and multimodal learning, e.g., in representation learning with image/caption pairs. While its effectiveness is not yet fully understood, a line of recent work reveals that contrastive learning can invert the data generating process and recov... | Published as a conference paper at ICLR 2023 IDENTIFIABILITY RESULTS FOR MULTIMODAL CONTRASTIVE LEARNING |
d252683720 | For long-tailed classification tasks, most works often pretrain a big model on a large-scale (unlabeled) dataset, and then fine-tune the whole pretrained model for adapting to long-tailed data. Though promising, fine-tuning the whole pretrained model tends to suffer from high cost in computation and deployment of diffe... | Published as a conference paper at ICLR 2023 LPT: LONG-TAILED PROMPT TUNING FOR IMAGE CLASSIFICATION |
d249848305 | The Receptive Field (RF) size has been one of the most important factors for One Dimensional Convolutional Neural Networks (1D-CNNs) on time series classification tasks. Large efforts have been taken to choose the appropriate size because it has a huge influence on the performance and differs significantly for each dat... | OMNI-SCALE CNNS: A SIMPLE AND EFFECTIVE KER- NEL SIZE CONFIGURATION FOR TIME SERIES CLASSIFI- CATION |
d257427177 | Existing large language model-based code generation pipelines typically use beam search or sampling algorithms during the decoding process. Although the programs they generate achieve high token-matching-based scores, they often fail to compile or generate incorrect outputs. The main reason is that conventional Transfo... | Published as a conference paper at ICLR 2023 PLANNING WITH LARGE LANGUAGE MODELS FOR CODE GENERATION |
d254125471 | Kernel matrices, as well as weighted graphs represented by them, are ubiquitous objects in machine learning, statistics and other related fields. The main drawback of using kernel methods (learning and inference using kernel matrices) is efficiency -given n input points, most kernel-based algorithms need to materialize... | Sub-quadratic Algorithms for Kernel Matrices via Kernel Density Estimation |
d247158489 | As Artificial Intelligence as a Service gains popularity, protecting well-trained models as intellectual property is becoming increasingly important. There are two common types of protection methods: ownership verification and usage authorization. In this paper, we propose Non-Transferable Learning (NTL), a novel appro... | NON-TRANSFERABLE LEARNING: A NEW APPROACH FOR MODEL OWNERSHIP VERIFICATION AND APPLI- CABILITY AUTHORIZATION |
d222133157 | Intelligent agents need to generalize from past experience to achieve goals in complex environments. World models facilitate such generalization and allow learning behaviors from imagined outcomes to increase sample-efficiency. While learning world models from image inputs has recently become feasible for some tasks, m... | Published as a conference paper at ICLR 2021 MASTERING ATARI WITH DISCRETE WORLD MODELS |
d256459301 | A fundamental challenge for machine learning models is how to generalize learned models for out-of-distribution (OOD) data. Among various approaches, exploiting invariant features by Domain Adversarial Training (DAT) received widespread attention. Despite its success, we observe training instability from DAT, mostly du... | Published as a conference paper at ICLR 2023 FREE LUNCH FOR DOMAIN ADVERSARIAL TRAINING: ENVIRONMENT LABEL SMOOTHING |
d237386137 | Recent years have witnessed a surging interest in Neural Architecture Search (NAS). Various algorithms have been proposed to improve the search efficiency and the search effectiveness of NAS, i.e., to reduce the search cost and improve the generalization performance of the selected architectures, respectively. However,... | Published as a conference paper at ICLR 2022 NASI: LABEL-AND DATA-AGNOSTIC NEURAL ARCHITECTURE SEARCH AT INITIALIZATION |
d258352681 | Robots operating in the real world require both rich manipulation skills as well as the ability to semantically reason about when to apply those skills. Towards this goal, recent works have integrated semantic representations from large-scale pretrained vision-language (VL) models into manipulation models, imparting th... | Published as a conference paper at ICLR 2023 PROGRAMMATICALLY GROUNDED, COMPOSITION- ALLY GENERALIZABLE ROBOTIC MANIPULATION |
d2647832 | We introduce Deep Linear Discriminant Analysis (DeepLDA) which learns linearly separable latent representations in an end-to-end fashion. Classic LDA extracts features which preserve class separability and is used for dimensionality reduction for many classification problems. The central idea of this paper is to put LD... | DEEP LINEAR DISCRIMINANT ANALYSIS |
d204874971 | Learning rich representation from data is an important task for deep generative models such as variational auto-encoder (VAE). However, by extracting high-level abstractions in the bottom-up inference process, the goal of preserving all factors of variations for top-down generation is compromised. Motivated by the conc... | Published as a conference paper at ICLR 2020 PROGRESSIVE LEARNING AND DISENTANGLEMENT OF HIERARCHICAL REPRESENTATIONS |
d208176134 | Handling missing data is one of the most fundamental problems in machine learning. Among many approaches, the simplest and most intuitive way is zero imputation, which treats the value of a missing entry simply as zero. However, many studies have experimentally confirmed that zero imputation results in suboptimal perfo... | Published as a conference paper at ICLR 2020 WHY NOT TO USE ZERO IMPUTATION? CORRECTING SPARSITY BIAS IN TRAINING NEURAL NETWORKS |
d244908227 | Vision Transformer (ViT) and its variants (e.g., Swin, PVT) have achieved great success in various computer vision tasks, owing to their capability to learn longrange contextual information. Layer Normalization (LN) is an essential ingredient in these models. However, we found that the ordinary LN makes tokens at diffe... | DYNAMIC TOKEN NORMALIZATION IMPROVES VI- SION TRANSFORMER |
d257496643 | Self-paced learning has been beneficial for tasks where some initial knowledge is available, such as weakly supervised learning and domain adaptation, to select and order the training sample sequence, from easy to complex. However its applicability remains unexplored in unsupervised learning, whereby the knowledge of t... | HYPERBOLIC SELF-PACED LEARNING FOR SELF- SUPERVISED SKELETON-BASED ACTION REPRESEN- TATIONS |
d232110691 | We propose a new simple approach for image compression: instead of storing the RGB values for each pixel of an image, we store the weights of a neural network overfitted to the image. Specifically, to encode an image, we fit it with an MLP which maps pixel locations to RGB values. We then quantize and store the weights... | COIN: COMPRESSION WITH IMPLICIT NEURAL REPRESENTATIONS |
d232427971 | We consider the task of enforcing individual fairness in gradient boosting. Gradient boosting is a popular method for machine learning from tabular data, which arise often in applications where algorithmic fairness is a concern. At a high level, our approach is a functional gradient descent on a (distributionally) robu... | Published as a conference paper at ICLR 2021 INDIVIDUALLY FAIR GRADIENT BOOSTING |
d16359184 | Learning Stable Group Invariant Representations with Convolutional Networks | |
d230084048 | Neural Ordinary Differential Equations (NODEs) use a neural network to model the instantaneous rate of change in the state of a system. However, despite their apparent suitability for dynamics-governed time-series, NODEs present a few disadvantages. First, they are unable to adapt to incoming data points, a fundamental... | Published as a conference paper at ICLR 2021 NEURAL ODE PROCESSES |
d260429228 | Sequences have become first class citizens in supervised learning thanks to the resurgence of recurrent neural networks. Many complex tasks that require mapping from or to a sequence of observations can now be formulated with the sequence-to-sequence (seq2seq) framework which employs the chain rule to efficiently repre... | ORDER MATTERS: SEQUENCE TO SEQUENCE FOR SETS |
d53107519 | The convergence rate and final performance of common deep learning models have significantly benefited from heuristics such as learning rate schedules, knowledge distillation, skip connections, and normalization layers. In the absence of theoretical underpinnings, controlled experiments aimed at explaining these strate... | A CLOSER LOOK AT DEEP LEARNING HEURISTICS: LEARNING RATE RESTARTS, WARMUP AND DISTILLA- TION |
d202712906 | An important research direction in machine learning has centered around developing meta-learning algorithms to tackle few-shot learning. An especially successful algorithm has been Model Agnostic Meta-Learning (MAML), a method that consists of two optimization loops, with the outer loop finding a meta-initialization, f... | Published as a conference paper at ICLR 2020 RAPID LEARNING OR FEATURE REUSE? TOWARDS UNDERSTANDING THE EFFECTIVENESS OF MAML |
d246411266 | Empirical risk minimization (ERM) is known to be non-robust in practice to distributional shift where the training and the test distributions are different. A suite of approaches, such as importance weighting, and variants of distributionally robust optimization (DRO), have been proposed to solve this problem. But a li... | Published as a conference paper at ICLR 2023 UNDERSTANDING WHY GENERALIZED REWEIGHTING DOES NOT IMPROVE OVER ERM |
d253155221 | When learning task-oriented dialogue (ToD) agents, reinforcement learning (RL) techniques can naturally be utilized to train dialogue strategies to achieve userspecific goals. Prior works mainly focus on adopting advanced RL techniques to train the ToD agents, while the design of the reward function is not well studied... | Published as a conference paper at ICLR 2023 FANTASTIC REWARDS AND HOW TO TAME THEM: A CASE STUDY ON REWARD LEARNING FOR TASK- ORIENTED DIALOGUE SYSTEMS |
d16142207 | In this paper, we propose a data representation model that demonstrates hierarchical feature learning using nsNMF. We extend unit algorithm into several layers. Experiments with document and image data successfully discovered feature hierarchies. We also prove that proposed method results in much better classification ... | Hierarchical Data Representation Model - Multi-layer NMF |
d239768649 | In this work, we propose a communication-efficient parameterization, FedPara, for federated learning (FL) to overcome the burdens on frequent model uploads and downloads. Our method re-parameterizes weight parameters of layers using low-rank weights followed by the Hadamard product. Compared to the conventional low-ran... | Published as a conference paper at ICLR 2022 FEDPARA: LOW-RANK HADAMARD PRODUCT FOR COMMUNICATION-EFFICIENT FEDERATED LEARNING |
d256194482 | Multi-view image compression plays a critical role in 3D-related applications. Existing methods adopt a predictive coding architecture, which requires joint encoding to compress the corresponding disparity as well as residual information. This demands collaboration among cameras and enforces the epipolar geometric cons... | Published as a conference paper at ICLR 2023 LDMIC: LEARNING-BASED DISTRIBUTED MULTI- VIEW IMAGE CODING |
d259977321 | In this work, we aim to learn dexterous manipulation of deformable objects using multi-fingered hands. Reinforcement learning approaches for dexterous rigid object manipulation would struggle in this setting due to the complexity of physics interaction with deformable objects. At the same time, previous trajectory opti... | Published as a conference paper at ICLR 2023 DEXDEFORM: DEXTEROUS DEFORMABLE OBJECT MANIPULATION WITH HUMAN DEMONSTRATIONS AND DIFFERENTIABLE PHYSICS |
d198953497 | In multiagent systems (MASs), each agent makes individual decisions but all of them contribute globally to the system evolution. Learning in MASs is difficult since each agent's selection of actions must take place in the presence of other co-learning agents. Moreover, the environmental stochasticity and uncertainties ... | Published as a conference paper at ICLR 2020 ACTION SEMANTICS NETWORK: CONSIDERING THE EFFECTS OF ACTIONS IN MULTIAGENT SYSTEMS |
d256416230 | Generating photo-realistic video portrait with arbitrary speech audio is a crucial problem in film-making and virtual reality. Recently, several works explore the usage of neural radiance field in this task to improve 3D realness and image fidelity. However, the generalizability of previous NeRF-based methods to out-of... | Published as a conference paper at ICLR 2023 GENEFACE: GENERALIZED AND HIGH-FIDELITY AUDIO-DRIVEN 3D TALKING FACE SYNTHESIS |
d212628243 | Active inference is a theory that underpins the way biological agent's perceive and act in the real world. At its core, active inference is based on the principle that the brain is an approximate Bayesian inference engine, building an internal generative model to drive agents towards minimal surprise. Although this the... | DEEP ACTIVE INFERENCE FOR AUTONOMOUS ROBOT NAVIGATION |
d252907411 | DescriptionSitting at the edge of the bed and facing the couch. Question q : Can I go straight to the coffee table in front of me? Scene context : 3D scan, egocentric video, birdeye view (BEV) picture, etc. Answer : No Location (optional): t t+1ABSTRACT We propose a new task to benchmark scene understanding of embodied... | Published as a conference paper at ICLR 2023 SQA3D: SITUATED QUESTION ANSWERING IN 3D SCENES |
d222133323 | Neural networks (NNs) whose subnetworks implement reusable functions are expected to offer numerous advantages, including compositionality through efficient recombination of functional building blocks, interpretability, preventing catastrophic interference, etc. Understanding if and how NNs are modular could provide in... | arxiv pre-print ARE NEURAL NETS MODULAR? INSPECTING FUNC- TIONAL MODULARITY THROUGH DIFFERENTIABLE WEIGHT MASKS |
d593434 | This paper introduces EXMOVES, learned exemplar-based features for efficient recognition of actions in videos. The entries in our descriptor are produced by evaluating a set of movement classifiers over spatial-temporal volumes of the input sequence. Each movement classifier is a simple exemplar-SVM trained on low-leve... | EXMOVES: Classifier-based Features for Scalable Action Recognition |
d220363877 | Training neural networks with auxiliary tasks is a common practice for improving the performance on a main task of interest. Two main challenges arise in this multi-task learning setting: (i) designing useful auxiliary tasks; and (ii) combining auxiliary tasks into a single coherent loss. Here, we propose a novel frame... | Published as a conference paper at ICLR 2021 AUXILIARY LEARNING BY IMPLICIT DIFFERENTIATION |
d233169075 | Humans can quickly adapt to new partners in collaborative tasks (e.g. playing basketball), because they understand which fundamental skills of the task (e.g. how to dribble, how to shoot) carry over across new partners. Humans can also quickly adapt to similar tasks with the same partners by carrying over conventions t... | Published as a conference paper at ICLR 2021 ON THE CRITICAL ROLE OF CONVENTIONS IN ADAPTIVE HUMAN-AI COLLABORATION |
d13574093 | We propose a new neurally-inspired model that can learn to encode global relationship context of visual events across time and space and to use the contextual information to modulate the analysis by synthesis process in a predictive coding framework.The model is based on the principle of mutual predictability.It learns... | Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction |
d159321124 | Legal Friction of State Civil Apparatus Neutrality in Indonesia | |
d53474174 | The impressive lifelong learning in animal brains is primarily enabled by plastic changes in synaptic connectivity. Importantly, these changes are not passive, but are actively controlled by neuromodulation, which is itself under the control of the brain. The resulting self-modifying abilities of the brain play an impo... | BACKPROPAMINE: TRAINING SELF-MODIFYING NEU- RAL NETWORKS WITH DIFFERENTIABLE NEUROMODU- LATED PLASTICITY |
d1136006 | Distance metric learning (DML) approaches learn a transformation to a representation space where distance is in correspondence with a predefined notion of similarity.While such models offer a number of compelling benefits, it has been difficult for these to compete with modern classification algorithms in performance a... | METRIC LEARNING WITH ADAPTIVE DENSITY DISCRIMINATION |
d219179868 | Advanced data augmentation strategies have widely been studied to improve the generalization ability of deep learning models. Regional dropout is one of the popular solutions that guides the model to focus on less discriminative parts by randomly removing image regions, resulting in improved regularization. However, su... | Published as a conference paper at ICLR 2021 SALIENCYMIX: A SALIENCY GUIDED DATA AUG- MENTATION STRATEGY FOR BETTER REGULARIZA- TION |
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