paper_id string | title string | paper_url string | authors list | type string | primary_area string | abstract large_string | keywords list | TL;DR large_string | submission_number int64 | arxiv_id string | arxiv_id_source string |
|---|---|---|---|---|---|---|---|---|---|---|---|
UH-cmocLJC | How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks | https://openreview.net/forum?id=UH-cmocLJC | [
"Keyulu Xu",
"Mozhi Zhang",
"Jingling Li",
"Simon Shaolei Du",
"Ken-Ichi Kawarabayashi",
"Stefanie Jegelka"
] | Oral | null | We study how neural networks trained by gradient descent extrapolate, i.e., what they learn outside the support of the training distribution. Previous works report mixed empirical results when extrapolating with neural networks: while feedforward neural networks, a.k.a. multilayer perceptrons (MLPs), do not extrapolat... | [
"extrapolation",
"deep learning",
"out-of-distribution",
"graph neural networks",
"deep learning theory"
] | null | 700 | 2009.11848 | title_snapshot |
D3PcGLdMx0 | MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning | https://openreview.net/forum?id=D3PcGLdMx0 | [
"Nanyi Fei",
"Zhiwu Lu",
"Tao Xiang",
"Songfang Huang"
] | Poster | null | Most recent few-shot learning (FSL) approaches are based on episodic training whereby each episode samples few training instances (shots) per class to imitate the test condition. However, this strict adhering to test condition has a negative side effect, that is, the trained model is susceptible to the poor sampling of... | [
"few-shot learning",
"episodic training",
"cross-episode attention"
] | null | 698 | null | null |
6BRLOfrMhW | Partitioned Learned Bloom Filters | https://openreview.net/forum?id=6BRLOfrMhW | [
"Kapil Vaidya",
"Eric Knorr",
"Michael Mitzenmacher",
"Tim Kraska"
] | Poster | null | Bloom filters are space-efficient probabilistic data structures that are used to test whether an element is a member of a set, and may return false positives. Recently, variations referred to as learned Bloom filters were developed that can provide improved performance in terms of the rate of false positives, by using... | [
"optimization",
"data structures",
"algorithms",
"theory",
"learned algorithms"
] | null | 696 | null | null |
AAes_3W-2z | Wasserstein Embedding for Graph Learning | https://openreview.net/forum?id=AAes_3W-2z | [
"Soheil Kolouri",
"Navid Naderializadeh",
"Gustavo K. Rohde",
"Heiko Hoffmann"
] | Poster | null | We present Wasserstein Embedding for Graph Learning (WEGL), a novel and fast framework for embedding entire graphs in a vector space, in which various machine learning models are applicable for graph-level prediction tasks. We leverage new insights on defining similarity between graphs as a function of the similarity b... | [
"Wasserstein",
"graph embedding",
"graph-level prediction"
] | null | 692 | 2006.09430 | title_snapshot |
MxaY4FzOTa | High-Capacity Expert Binary Networks | https://openreview.net/forum?id=MxaY4FzOTa | [
"Adrian Bulat",
"Brais Martinez",
"Georgios Tzimiropoulos"
] | Poster | null | Network binarization is a promising hardware-aware direction for creating efficient deep models. Despite its memory and computational advantages, reducing the accuracy gap between binary models and their real-valued counterparts remains an unsolved challenging research problem. To this end, we make the following 3 cont... | [] | null | 690 | 2010.03558 | title_snapshot |
Cz3dbFm5u- | SAFENet: A Secure, Accurate and Fast Neural Network Inference | https://openreview.net/forum?id=Cz3dbFm5u- | [
"Qian Lou",
"Yilin Shen",
"Hongxia Jin",
"Lei Jiang"
] | Poster | null | The advances in neural networks have driven many companies to provide prediction services to users in a wide range of applications. However, current prediction systems raise privacy concerns regarding the user's private data. A cryptographic neural network inference service is an efficient way to allow two parties to e... | [
"Cryptographic inference",
"Channel-Wise Approximated Activation",
"Hyper-Parameter Optimization",
"Garbled Circuits"
] | null | 686 | null | null |
Gu5WqN9J3Fn | Learning Manifold Patch-Based Representations of Man-Made Shapes | https://openreview.net/forum?id=Gu5WqN9J3Fn | [
"Dmitriy Smirnov",
"Mikhail Bessmeltsev",
"Justin Solomon"
] | Poster | null | Choosing the right representation for geometry is crucial for making 3D models compatible with existing applications. Focusing on piecewise-smooth man-made shapes, we propose a new representation that is usable in conventional CAD modeling pipelines and can also be learned by deep neural networks. We demonstrate its be... | [
"3D shape representations",
"CAD modeling",
"sketch-based modeling",
"computer graphics",
"computer vision",
"deep learning"
] | null | 685 | 1906.12337 | title_snapshot |
-IXhmY16R3M | Universal approximation power of deep residual neural networks via nonlinear control theory | https://openreview.net/forum?id=-IXhmY16R3M | [
"Paulo Tabuada",
"Bahman Gharesifard"
] | Poster | null | In this paper, we explain the universal approximation capabilities of deep residual neural networks through geometric nonlinear control. Inspired by recent work establishing links between residual networks and control systems, we provide a general sufficient condition for a residual network to have the power of univers... | [
"Deep residual neural networks",
"universal approximation",
"nonlinear control theory"
] | null | 683 | 2007.06007 | title_snapshot |
kW_zpEmMLdP | Learning Neural Event Functions for Ordinary Differential Equations | https://openreview.net/forum?id=kW_zpEmMLdP | [
"Ricky T. Q. Chen",
"Brandon Amos",
"Maximilian Nickel"
] | Poster | null | The existing Neural ODE formulation relies on an explicit knowledge of the termination time. We extend Neural ODEs to implicitly defined termination criteria modeled by neural event functions, which can be chained together and differentiated through. Neural Event ODEs are capable of modeling discrete and instantaneous ... | [
"differential equations",
"implicit differentiation",
"point processes"
] | null | 666 | 2011.03902 | title_snapshot |
XQQA6-So14 | Neural Spatio-Temporal Point Processes | https://openreview.net/forum?id=XQQA6-So14 | [
"Ricky T. Q. Chen",
"Brandon Amos",
"Maximilian Nickel"
] | Poster | null | We propose a new class of parameterizations for spatio-temporal point processes which leverage Neural ODEs as a computational method and enable flexible, high-fidelity models of discrete events that are localized in continuous time and space. Central to our approach is a combination of continuous-time neural networks w... | [
"point processes",
"normalizing flows",
"differential equations"
] | null | 665 | 2011.04583 | title_snapshot |
LVotkZmYyDi | Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry | https://openreview.net/forum?id=LVotkZmYyDi | [
"Ziyi Chen",
"Yi Zhou",
"Tengyu Xu",
"Yingbin Liang"
] | Poster | null | The gradient descent-ascent (GDA) algorithm has been widely applied to solve minimax optimization problems. In order to achieve convergent policy parameters for minimax optimization, it is important that GDA generates convergent variable sequences rather than convergent sequences of function value or gradient norm. How... | [
"Kurdyka-Łojasiewicz geometry",
"minimax",
"nonconvex",
"proximal gradient descent-ascent",
"variable convergence"
] | null | 664 | 2102.04653 | title_judge |
n6jl7fLxrP | Adaptive Universal Generalized PageRank Graph Neural Network | https://openreview.net/forum?id=n6jl7fLxrP | [
"Eli Chien",
"Jianhao Peng",
"Pan Li",
"Olgica Milenkovic"
] | Poster | null | In many important graph data processing applications the acquired information includes both node features and observations of the graph topology. Graph neural networks (GNNs) are designed to exploit both sources of evidence but they do not optimally trade-off their utility and integrate them in a manner that is also un... | [
"Graph Neural Networks",
"Generalized PageRank",
"Heterophily",
"Homophily",
"Over-smoothing"
] | null | 663 | 2006.07988 | title_snapshot |
MmCRswl1UYl | Open Question Answering over Tables and Text | https://openreview.net/forum?id=MmCRswl1UYl | [
"Wenhu Chen",
"Ming-Wei Chang",
"Eva Schlinger",
"William Yang Wang",
"William W. Cohen"
] | Poster | null | In open question answering (QA), the answer to a question is produced by retrieving and then analyzing documents that might contain answers to the question. Most open QA systems have considered only retrieving information from unstructured text. Here we consider for the first time open QA over {\em both} tabular and ... | [
"Question Answering",
"Tabular Data",
"Open-domain",
"Retrieval"
] | null | 662 | 2010.10439 | title_snapshot |
Ua6zuk0WRH | Rethinking Attention with Performers | https://openreview.net/forum?id=Ua6zuk0WRH | [
"Krzysztof Marcin Choromanski",
"Valerii Likhosherstov",
"David Dohan",
"Xingyou Song",
"Andreea Gane",
"Tamas Sarlos",
"Peter Hawkins",
"Jared Quincy Davis",
"Afroz Mohiuddin",
"Lukasz Kaiser",
"David Benjamin Belanger",
"Lucy J Colwell",
"Adrian Weller"
] | Oral | null | We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors such as sparsity or low-rankness. To approximate softmax attention-ker... | [
"performer",
"transformer",
"attention",
"softmax",
"approximation",
"linear",
"bert",
"bidirectional",
"unidirectional",
"orthogonal",
"random",
"features",
"FAVOR",
"kernel",
"generalized",
"sparsity",
"reformer",
"linformer",
"protein",
"trembl",
"uniprot"
] | null | 656 | 2009.14794 | title_snapshot |
RovX-uQ1Hua | Text Generation by Learning from Demonstrations | https://openreview.net/forum?id=RovX-uQ1Hua | [
"Richard Yuanzhe Pang",
"He He"
] | Poster | null | Current approaches to text generation largely rely on autoregressive models and maximum likelihood estimation. This paradigm leads to (i) diverse but low-quality samples due to mismatched learning objective and evaluation metric (likelihood vs. quality) and (ii) exposure bias due to mismatched history distributions (go... | [
"text generation",
"learning from demonstrations",
"nlp"
] | null | 655 | 2009.07839 | title_snapshot |
EqoXe2zmhrh | Support-set bottlenecks for video-text representation learning | https://openreview.net/forum?id=EqoXe2zmhrh | [
"Mandela Patrick",
"Po-Yao Huang",
"Yuki Asano",
"Florian Metze",
"Alexander G Hauptmann",
"Joao F. Henriques",
"Andrea Vedaldi"
] | Spotlight | null | The dominant paradigm for learning video-text representations – noise contrastive learning – increases the similarity of the representations of pairs of samples that are known to be related, such as text and video from the same sample, and pushes away the representations of all other pairs. We posit that this last beha... | [
"video representation learning",
"multi-modal learning",
"video-text learning",
"contrastive learning"
] | null | 649 | 2010.02824 | title_snapshot |
K5YasWXZT3O | Tilted Empirical Risk Minimization | https://openreview.net/forum?id=K5YasWXZT3O | [
"Tian Li",
"Ahmad Beirami",
"Maziar Sanjabi",
"Virginia Smith"
] | Poster | null | Empirical risk minimization (ERM) is typically designed to perform well on the average loss, which can result in estimators that are sensitive to outliers, generalize poorly, or treat subgroups unfairly. While many methods aim to address these problems individually, in this work, we explore them through a unified frame... | [
"exponential tilting",
"models of learning and generalization",
"label noise robustness",
"fairness"
] | null | 647 | 2007.01162 | title_snapshot |
pGIHq1m7PU | Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs | https://openreview.net/forum?id=pGIHq1m7PU | [
"Zhen Han",
"Peng Chen",
"Yunpu Ma",
"Volker Tresp"
] | Poster | null | Modeling time-evolving knowledge graphs (KGs) has recently gained increasing interest. Here, graph representation learning has become the dominant paradigm for link prediction on temporal KGs. However, the embedding-based approaches largely operate in a black-box fashion, lacking the ability to interpret their predicti... | [
"Temporal knowledge graph",
"future link prediction",
"graph neural network",
"subgraph reasoning."
] | null | 644 | 2012.15537 | title_judge |
wpSWuz_hyqA | Grounded Language Learning Fast and Slow | https://openreview.net/forum?id=wpSWuz_hyqA | [
"Felix Hill",
"Olivier Tieleman",
"Tamara von Glehn",
"Nathaniel Wong",
"Hamza Merzic",
"Stephen Clark"
] | Spotlight | null | Recent work has shown that large text-based neural language models acquire a surprising propensity for one-shot learning. Here, we show that an agent situated in a simulated 3D world, and endowed with a novel dual-coding external memory, can exhibit similar one-shot word learning when trained with conventional RL algor... | [
"language",
"cognition",
"fast-mapping",
"grounding",
"word-learning",
"memory",
"meta-learning"
] | null | 643 | 2009.01719 | title_snapshot |
ufZN2-aehFa | Bayesian Context Aggregation for Neural Processes | https://openreview.net/forum?id=ufZN2-aehFa | [
"Michael Volpp",
"Fabian Flürenbrock",
"Lukas Grossberger",
"Christian Daniel",
"Gerhard Neumann"
] | Poster | null | Formulating scalable probabilistic regression models with reliable uncertainty estimates has been a long-standing challenge in machine learning research. Recently, casting probabilistic regression as a multi-task learning problem in terms of conditional latent variable (CLV) models such as the Neural Process (NP) has ... | [
"Aggregation Methods",
"Neural Processes",
"Latent Variable Models",
"Meta Learning",
"Multi-task Learning",
"Deep Sets"
] | null | 637 | null | null |
q-cnWaaoUTH | Conformation-Guided Molecular Representation with Hamiltonian Neural Networks | https://openreview.net/forum?id=q-cnWaaoUTH | [
"Ziyao Li",
"Shuwen Yang",
"Guojie Song",
"Lingsheng Cai"
] | Poster | null | Well-designed molecular representations (fingerprints) are vital to combine medical chemistry and deep learning. Whereas incorporating 3D geometry of molecules (i.e. conformations) in their representations seems beneficial, current 3D algorithms are still in infancy. In this paper, we propose a novel molecular represen... | [
"Molecular Representation",
"Neural Physics Engines",
"Molecular Dynamics",
"Graph Neural Networks"
] | null | 636 | 2105.03688 | title_judge |
zDy_nQCXiIj | GAN "Steerability" without optimization | https://openreview.net/forum?id=zDy_nQCXiIj | [
"Nurit Spingarn",
"Ron Banner",
"Tomer Michaeli"
] | Spotlight | null | Recent research has shown remarkable success in revealing "steering" directions in the latent spaces of pre-trained GANs. These directions correspond to semantically meaningful image transformations (e.g., shift, zoom, color manipulations), and have the same interpretable effect across all categories that the GAN can g... | [
"Generative Adversarial Network",
"semantic directions in latent space",
"nonlinear walk"
] | null | 635 | 2012.05328 | title_snapshot |
ETBc_MIMgoX | Learning with AMIGo: Adversarially Motivated Intrinsic Goals | https://openreview.net/forum?id=ETBc_MIMgoX | [
"Andres Campero",
"Roberta Raileanu",
"Heinrich Kuttler",
"Joshua B. Tenenbaum",
"Tim Rocktäschel",
"Edward Grefenstette"
] | Poster | null | A key challenge for reinforcement learning (RL) consists of learning in environments with sparse extrinsic rewards. In contrast to current RL methods, humans are able to learn new skills with little or no reward by using various forms of intrinsic motivation. We propose AMIGo, a novel agent incorporating -- as form of ... | [
"reinforcement learning",
"exploration",
"meta-learning"
] | null | 631 | 2006.12122 | title_snapshot |
dV19Yyi1fS3 | Training with Quantization Noise for Extreme Model Compression | https://openreview.net/forum?id=dV19Yyi1fS3 | [
"Pierre Stock",
"Angela Fan",
"Benjamin Graham",
"Edouard Grave",
"Rémi Gribonval",
"Herve Jegou",
"Armand Joulin"
] | Poster | null | We tackle the problem of producing compact models, maximizing their accuracy for a given model size. A standard solution is to train networks with Quantization Aware Training, where the weights are quantized during training and the gradients approximated with the Straight-Through Estimator. In this paper, we extend thi... | [
"Compression",
"Efficiency",
"Product Quantization"
] | null | 630 | 2004.07320 | title_snapshot |
Jacdvfjicf7 | Interpreting and Boosting Dropout from a Game-Theoretic View | https://openreview.net/forum?id=Jacdvfjicf7 | [
"Hao Zhang",
"Sen Li",
"YinChao Ma",
"Mingjie Li",
"Yichen Xie",
"Quanshi Zhang"
] | Poster | null | This paper aims to understand and improve the utility of the dropout operation from the perspective of game-theoretical interactions. We prove that dropout can suppress the strength of interactions between input variables of deep neural networks (DNNs). The theoretical proof is also verified by various experiments. Fur... | [
"Dropout",
"Interpretability",
"Interactions"
] | null | 627 | 2009.11729 | title_snapshot |
DILxQP08O3B | VTNet: Visual Transformer Network for Object Goal Navigation | https://openreview.net/forum?id=DILxQP08O3B | [
"Heming Du",
"Xin Yu",
"Liang Zheng"
] | Poster | null | Object goal navigation aims to steer an agent towards a target object based on observations of the agent. It is of pivotal importance to design effective visual representations of the observed scene in determining navigation actions. In this paper, we introduce a Visual Transformer Network (VTNet) for learning informa... | [] | null | 624 | 2105.09447 | title_snapshot |
QO9-y8also- | Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization | https://openreview.net/forum?id=QO9-y8also- | [
"Judy Borowski",
"Roland Simon Zimmermann",
"Judith Schepers",
"Robert Geirhos",
"Thomas S. A. Wallis",
"Matthias Bethge",
"Wieland Brendel"
] | Poster | null | Feature visualizations such as synthetic maximally activating images are a widely used explanation method to better understand the information processing of convolutional neural networks (CNNs). At the same time, there are concerns that these visualizations might not accurately represent CNNs' inner workings. Here, we ... | [
"evaluation of interpretability",
"feature visualization",
"activation maximization",
"human psychophysics",
"understanding CNNs",
"explanation method"
] | null | 621 | 2010.12606 | title_snapshot |
AICNpd8ke-m | Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning | https://openreview.net/forum?id=AICNpd8ke-m | [
"Kanil Patel",
"William H. Beluch",
"Bin Yang",
"Michael Pfeiffer",
"Dan Zhang"
] | Poster | null | Post-hoc multi-class calibration is a common approach for providing high-quality confidence estimates of deep neural network predictions. Recent work has shown that widely used scaling methods underestimate their calibration error, while alternative Histogram Binning (HB) methods often fail to preserve classification a... | [
"uncertainty calibration",
"post-hoc calibration",
"histogram binning",
"mutual information",
"deep neural networks"
] | null | 600 | 2006.13092 | title_snapshot |
-_Zp7r2-cGK | A Discriminative Gaussian Mixture Model with Sparsity | https://openreview.net/forum?id=-_Zp7r2-cGK | [
"Hideaki Hayashi",
"Seiichi Uchida"
] | Poster | null | In probabilistic classification, a discriminative model based on the softmax function has a potential limitation in that it assumes unimodality for each class in the feature space. The mixture model can address this issue, although it leads to an increase in the number of parameters. We propose a sparse classifier base... | [
"classification",
"sparse Bayesian learning",
"Gaussian mixture model"
] | null | 595 | 1911.06028 | title_snapshot |
OOsR8BzCnl5 | Trusted Multi-View Classification | https://openreview.net/forum?id=OOsR8BzCnl5 | [
"Zongbo Han",
"Changqing Zhang",
"Huazhu Fu",
"Joey Tianyi Zhou"
] | Poster | null | Multi-view classification (MVC) generally focuses on improving classification accuracy by using information from different views, typically integrating them into a unified comprehensive representation for downstream tasks. However, it is also crucial to dynamically assess the quality of a view for different samples in ... | [
"Multi-Modal Learning",
"Multi-View Learning",
"Uncertainty Machine Learning"
] | null | 591 | 2102.02051 | title_snapshot |
xzqLpqRzxLq | IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot Learning | https://openreview.net/forum?id=xzqLpqRzxLq | [
"Manli Zhang",
"Jianhong Zhang",
"Zhiwu Lu",
"Tao Xiang",
"Mingyu Ding",
"Songfang Huang"
] | Poster | null | The need of collecting large quantities of labeled training data for each new task has limited the usefulness of deep neural networks. Given data from a set of source tasks, this limitation can be overcome using two transfer learning approaches: few-shot learning (FSL) and self-supervised learning (SSL). The former aim... | [
"few-shot learning",
"self-supervised learning",
"episode-level pretext task"
] | null | 587 | null | null |
nIAxjsniDzg | What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study | https://openreview.net/forum?id=nIAxjsniDzg | [
"Marcin Andrychowicz",
"Anton Raichuk",
"Piotr Stańczyk",
"Manu Orsini",
"Sertan Girgin",
"Raphaël Marinier",
"Leonard Hussenot",
"Matthieu Geist",
"Olivier Pietquin",
"Marcin Michalski",
"Sylvain Gelly",
"Olivier Bachem"
] | Oral | null | In recent years, reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple, their state-of-the-art implementations take numerous low- and high-level design decisions that strongly affect the performance of the resulting agents... | [
"Reinforcement learning",
"continuous control"
] | null | 581 | null | null |
KpfasTaLUpq | Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation | https://openreview.net/forum?id=KpfasTaLUpq | [
"Jungo Kasai",
"Nikolaos Pappas",
"Hao Peng",
"James Cross",
"Noah Smith"
] | Poster | null | Much recent effort has been invested in non-autoregressive neural machine translation, which appears to be an efficient alternative to state-of-the-art autoregressive machine translation on modern GPUs. In contrast to the latter, where generation is sequential, the former allows generation to be parallelized across ta... | [
"Machine Translation",
"Sequence Modeling",
"Natural Language Processing"
] | null | 579 | 2006.10369 | title_snapshot |
ldxlzGYWDmW | Effective Abstract Reasoning with Dual-Contrast Network | https://openreview.net/forum?id=ldxlzGYWDmW | [
"Tao Zhuo",
"Mohan Kankanhalli"
] | Poster | null | As a step towards improving the abstract reasoning capability of machines, we aim to solve Raven’s Progressive Matrices (RPM) with neural networks, since solving RPM puzzles is highly correlated with human intelligence. Unlike previous methods that use auxiliary annotations or assume hidden rules to produce appropriate... | [
"abstract reasoning",
"raven's progressive matrices",
"deep learning"
] | null | 576 | 2205.13720 | title_snapshot |
80FMcTSZ6J0 | Noise against noise: stochastic label noise helps combat inherent label noise | https://openreview.net/forum?id=80FMcTSZ6J0 | [
"Pengfei Chen",
"Guangyong Chen",
"Junjie Ye",
"jingwei zhao",
"Pheng-Ann Heng"
] | Spotlight | null | The noise in stochastic gradient descent (SGD) provides a crucial implicit regularization effect, previously studied in optimization by analyzing the dynamics of parameter updates. In this paper, we are interested in learning with noisy labels, where we have a collection of samples with potential mislabeling. We show t... | [
"Noisy Labels",
"Robust Learning",
"SGD noise",
"Regularization"
] | null | 568 | null | null |
5m3SEczOV8L | VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models | https://openreview.net/forum?id=5m3SEczOV8L | [
"Zhisheng Xiao",
"Karsten Kreis",
"Jan Kautz",
"Arash Vahdat"
] | Spotlight | null | Energy-based models (EBMs) have recently been successful in representing complex distributions of small images. However, sampling from them requires expensive Markov chain Monte Carlo (MCMC) iterations that mix slowly in high dimensional pixel space. Unlike EBMs, variational autoencoders (VAEs) generate samples quickly... | [
"Energy-based Models",
"Variational Auto-encoder",
"MCMC"
] | null | 562 | 2010.00654 | title_snapshot |
onxoVA9FxMw | On Position Embeddings in BERT | https://openreview.net/forum?id=onxoVA9FxMw | [
"Benyou Wang",
"Lifeng Shang",
"Christina Lioma",
"Xin Jiang",
"Hao Yang",
"Qun Liu",
"Jakob Grue Simonsen"
] | Poster | null | Various Position Embeddings (PEs) have been proposed in Transformer based architectures~(e.g. BERT) to model word order. These are empirically-driven and perform well, but no formal framework exists to systematically study them. To address this, we present three properties of PEs that capture word distance in vector sp... | [
"Position Embedding",
"BERT",
"pretrained language model."
] | null | 557 | null | null |
o966_Is_nPA | Neural Pruning via Growing Regularization | https://openreview.net/forum?id=o966_Is_nPA | [
"Huan Wang",
"Can Qin",
"Yulun Zhang",
"Yun Fu"
] | Poster | null | Regularization has long been utilized to learn sparsity in deep neural network pruning. However, its role is mainly explored in the small penalty strength regime. In this work, we extend its application to a new scenario where the regularization grows large gradually to tackle two central problems of pruning: pruning s... | [
"model compression",
"deep neural network pruning",
"Hessian matrix",
"regularization"
] | null | 555 | 2012.09243 | title_snapshot |
l-LGlk4Yl6G | Mixed-Features Vectors and Subspace Splitting | https://openreview.net/forum?id=l-LGlk4Yl6G | [
"Alejandro Pimentel-Alarcón",
"Daniel L. Pimentel-Alarcón"
] | Poster | null | Motivated by metagenomics, recommender systems, dictionary learning, and related problems, this paper introduces subspace splitting(SS): the task of clustering the entries of what we call amixed-features vector, that is, a vector whose subsets of coordinates agree with a collection of subspaces. We derive precise ident... | [] | null | 549 | null | null |
r-gPPHEjpmw | Hierarchical Reinforcement Learning by Discovering Intrinsic Options | https://openreview.net/forum?id=r-gPPHEjpmw | [
"Jesse Zhang",
"Haonan Yu",
"Wei Xu"
] | Poster | null | We propose a hierarchical reinforcement learning method, HIDIO, that can learn task-agnostic options in a self-supervised manner while jointly learning to utilize them to solve sparse-reward tasks. Unlike current hierarchical RL approaches that tend to formulate goal-reaching low-level tasks or pre-define ad hoc lower-... | [
"hierarchical reinforcement learning",
"reinforcement learning",
"options",
"unsupervised skill discovery",
"exploration"
] | null | 548 | 2101.06521 | title_snapshot |
r28GdiQF7vM | Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions | https://openreview.net/forum?id=r28GdiQF7vM | [
"Yunwen Lei",
"Yiming Ying"
] | Poster | null | Stochastic optimization has become the workhorse behind many successful machine learning applications, which motivates a lot of theoretical analysis to understand its empirical behavior. As a comparison, there is far less work to study the generalization behavior especially in a non-convex learning setting. In this pap... | [
"generalization bounds",
"non-convex learning"
] | null | 546 | null | null |
Naqw7EHIfrv | Representation Learning for Sequence Data with Deep Autoencoding Predictive Components | https://openreview.net/forum?id=Naqw7EHIfrv | [
"Junwen Bai",
"Weiran Wang",
"Yingbo Zhou",
"Caiming Xiong"
] | Poster | null | We propose Deep Autoencoding Predictive Components (DAPC) -- a self-supervised representation learning method for sequence data, based on the intuition that useful representations of sequence data should exhibit a simple structure in the latent space. We encourage this latent structure by maximizing an estimate of \emp... | [
"Mutual Information",
"Unsupervised Learning",
"Sequence Data",
"Masked Reconstruction"
] | null | 544 | 2010.03135 | title_snapshot |
H0syOoy3Ash | Average-case Acceleration for Bilinear Games and Normal Matrices | https://openreview.net/forum?id=H0syOoy3Ash | [
"Carles Domingo-Enrich",
"Fabian Pedregosa",
"Damien Scieur"
] | Poster | null | Advances in generative modeling and adversarial learning have given rise to renewed interest in smooth games. However, the absence of symmetry in the matrix of second derivatives poses challenges that are not present in the classical minimization framework. While a rich theory of average-case analysis has been develope... | [
"Smooth games",
"First-order Methods",
"Acceleration",
"Bilinear games",
"Average-case Analysis",
"Orthogonal Polynomials"
] | null | 540 | 2010.02076 | title_snapshot |
HHSEKOnPvaO | Graph-Based Continual Learning | https://openreview.net/forum?id=HHSEKOnPvaO | [
"Binh Tang",
"David S. Matteson"
] | Spotlight | null | Despite significant advances, continual learning models still suffer from catastrophic forgetting when exposed to incrementally available data from non-stationary distributions. Rehearsal approaches alleviate the problem by maintaining and replaying a small episodic memory of previous samples, often implemented as an a... | [] | null | 536 | 2007.04813 | title_snapshot |
qzBUIzq5XR2 | Learning Task-General Representations with Generative Neuro-Symbolic Modeling | https://openreview.net/forum?id=qzBUIzq5XR2 | [
"Reuben Feinman",
"Brenden M. Lake"
] | Poster | null | People can learn rich, general-purpose conceptual representations from only raw perceptual inputs. Current machine learning approaches fall well short of these human standards, although different modeling traditions often have complementary strengths. Symbolic models can capture the compositional and causal knowledge t... | [
"few-shot concept learning",
"neuro-symbolic models",
"probabilistic programs",
"generative models"
] | null | 535 | 2006.14448 | title_snapshot |
PObuuGVrGaZ | Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study | https://openreview.net/forum?id=PObuuGVrGaZ | [
"Zhiqiang Shen",
"Zechun Liu",
"Dejia Xu",
"Zitian Chen",
"Kwang-Ting Cheng",
"Marios Savvides"
] | Poster | null | This work aims to empirically clarify a recently discovered perspective that label smoothing is incompatible with knowledge distillation. We begin by introducing the motivation behind on how this incompatibility is raised, i.e., label smoothing erases relative information between teacher logits. We provide a novel conn... | [
"label smoothing",
"knowledge distillation",
"image classification",
"neural machine translation",
"binary neural networks"
] | null | 530 | 2104.00676 | title_snapshot |
pBqLS-7KYAF | Sparse Quantized Spectral Clustering | https://openreview.net/forum?id=pBqLS-7KYAF | [
"Zhenyu Liao",
"Romain Couillet",
"Michael W. Mahoney"
] | Spotlight | null | Given a large data matrix, sparsifying, quantizing, and/or performing other entry-wise nonlinear operations can have numerous benefits, ranging from speeding up iterative algorithms for core numerical linear algebra problems to providing nonlinear filters to design state-of-the-art neural network models. Here, we explo... | [
"Eigenspectrum",
"high-dimensional statistic",
"random matrix theory",
"spectral clustering"
] | null | 525 | 2010.01376 | title_snapshot |
aYuZO9DIdnn | The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods | https://openreview.net/forum?id=aYuZO9DIdnn | [
"Louis THIRY",
"Michael Arbel",
"Eugene Belilovsky",
"Edouard Oyallon"
] | Poster | null | A recent line of work showed that various forms of convolutional kernel methods can be competitive with standard supervised deep convolutional networks on datasets like CIFAR-10, obtaining accuracies in the range of 87-90% while being more amenable to theoretical analysis. In this work, we highlight the importance of... | [
"convolutional kernel methods",
"image classification"
] | null | 523 | 2101.07528 | title_snapshot |
uxpzitPEooJ | Graph Coarsening with Neural Networks | https://openreview.net/forum?id=uxpzitPEooJ | [
"Chen Cai",
"Dingkang Wang",
"Yusu Wang"
] | Poster | null | As large scale-graphs become increasingly more prevalent, it poses significant computational challenges to process, extract and analyze large graph data. Graph coarsening is one popular technique to reduce the size of a graph while maintaining essential properties. Despite rich graph coarsening literature, there is onl... | [
"graph coarsening",
"graph neural network",
"Doubly-weighted Laplace operator"
] | null | 522 | 2102.01350 | title_snapshot |
0IO5VdnSAaH | On the Universality of the Double Descent Peak in Ridgeless Regression | https://openreview.net/forum?id=0IO5VdnSAaH | [
"David Holzmüller"
] | Poster | null | We prove a non-asymptotic distribution-independent lower bound for the expected mean squared generalization error caused by label noise in ridgeless linear regression. Our lower bound generalizes a similar known result to the overparameterized (interpolating) regime. In contrast to most previous works, our analysis app... | [
"Double Descent",
"Interpolation Peak",
"Linear Regression",
"Random Features",
"Random Weights Neural Networks"
] | null | 519 | 2010.01851 | title_snapshot |
Yz-XtK5RBxB | Deep Repulsive Clustering of Ordered Data Based on Order-Identity Decomposition | https://openreview.net/forum?id=Yz-XtK5RBxB | [
"Seon-Ho Lee",
"Chang-Su Kim"
] | Poster | null | We propose the deep repulsive clustering (DRC) algorithm of ordered data for effective order learning. First, we develop the order-identity decomposition (ORID) network to divide the information of an object instance into an order-related feature and an identity feature. Then, we group object instances into clusters ac... | [
"Clustering",
"order learning",
"age estimation",
"aesthetic assessment",
"historical color image classification"
] | null | 516 | null | null |
F-mvpFpn_0q | Rapid Task-Solving in Novel Environments | https://openreview.net/forum?id=F-mvpFpn_0q | [
"Samuel Ritter",
"Ryan Faulkner",
"Laurent Sartran",
"Adam Santoro",
"Matthew Botvinick",
"David Raposo"
] | Poster | null | We propose the challenge of rapid task-solving in novel environments (RTS), wherein an agent must solve a series of tasks as rapidly as possible in an unfamiliar environment. An effective RTS agent must balance between exploring the unfamiliar environment and solving its current task, all while building a model of the ... | [
"deep reinforcement learning",
"meta learning",
"deep learning",
"exploration",
"planning"
] | null | 515 | 2006.03662 | title_snapshot |
WAISmwsqDsb | DINO: A Conditional Energy-Based GAN for Domain Translation | https://openreview.net/forum?id=WAISmwsqDsb | [
"Konstantinos Vougioukas",
"Stavros Petridis",
"Maja Pantic"
] | Poster | null | Domain translation is the process of transforming data from one domain to another while preserving the common semantics. Some of the most popular domain translation systems are based on conditional generative adversarial networks, which use source domain data to drive the generator and as an input to the discriminator.... | [
"Generative Modelling",
"Domain Translation",
"Conditional GANs",
"Energy-Based GANs"
] | null | 509 | 2102.09281 | title_snapshot |
eIHYL6fpbkA | Removing Undesirable Feature Contributions Using Out-of-Distribution Data | https://openreview.net/forum?id=eIHYL6fpbkA | [
"Saehyung Lee",
"Changhwa Park",
"Hyungyu Lee",
"Jihun Yi",
"Jonghyun Lee",
"Sungroh Yoon"
] | Poster | null | Several data augmentation methods deploy unlabeled-in-distribution (UID) data to bridge the gap between the training and inference of neural networks. However, these methods have clear limitations in terms of availability of UID data and dependence of algorithms on pseudo-labels. Herein, we propose a data augmentation ... | [
"adversarial training",
"adversarial robustness",
"generalization",
"out-of-distribution"
] | null | 507 | 2101.06639 | title_snapshot |
JHcqXGaqiGn | Accurate Learning of Graph Representations with Graph Multiset Pooling | https://openreview.net/forum?id=JHcqXGaqiGn | [
"Jinheon Baek",
"Minki Kang",
"Sung Ju Hwang"
] | Poster | null | Graph neural networks have been widely used on modeling graph data, achieving impressive results on node classification and link prediction tasks. Yet, obtaining an accurate representation for a graph further requires a pooling function that maps a set of node representations into a compact form. A simple sum or averag... | [
"Graph representation learning",
"Graph pooling"
] | null | 495 | 2102.11533 | title_snapshot |
ce6CFXBh30h | Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning | https://openreview.net/forum?id=ce6CFXBh30h | [
"Wonyong Jeong",
"Jaehong Yoon",
"Eunho Yang",
"Sung Ju Hwang"
] | Poster | null | While existing federated learning approaches mostly require that clients have fully-labeled data to train on, in realistic settings, data obtained at the client-side often comes without any accompanying labels. Such deficiency of labels may result from either high labeling cost, or difficulty of annotation due to the r... | [
"Federated Learning"
] | null | 492 | 2006.12097 | title_snapshot |
Wga_hrCa3P3 | Contrastive Learning with Adversarial Perturbations for Conditional Text Generation | https://openreview.net/forum?id=Wga_hrCa3P3 | [
"Seanie Lee",
"Dong Bok Lee",
"Sung Ju Hwang"
] | Poster | null | Recently, sequence-to-sequence (seq2seq) models with the Transformer architecture have achieved remarkable performance on various conditional text generation tasks, such as machine translation. However, most of them are trained with teacher forcing with the ground truth label given at each time step, without being expo... | [
"conditional text generation",
"contrastive learning"
] | null | 491 | 2012.07280 | title_snapshot |
FZ1oTwcXchK | Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks | https://openreview.net/forum?id=FZ1oTwcXchK | [
"Shikuang Deng",
"Shi Gu"
] | Poster | null | Spiking neural networks (SNNs) are biology-inspired artificial neural networks (ANNs) that comprise of spiking neurons to process asynchronous discrete signals. While more efficient in power consumption and inference speed on the neuromorphic hardware, SNNs are usually difficult to train directly from scratch with spik... | [
"spiking neural network",
"weight balance",
"second-order approximation"
] | null | 484 | 2103.00476 | title_snapshot |
EKV158tSfwv | Efficient Continual Learning with Modular Networks and Task-Driven Priors | https://openreview.net/forum?id=EKV158tSfwv | [
"Tom Veniat",
"Ludovic Denoyer",
"MarcAurelio Ranzato"
] | Poster | null | Existing literature in Continual Learning (CL) has focused on overcoming catastrophic forgetting, the inability of the learner to recall how to perform tasks observed in the past.
There are however other desirable properties of a CL system, such as the ability to transfer knowledge from previous tasks and to scale mem... | [
"Continual learning",
"Lifelong learning",
"Benchmark",
"Modular network",
"Neural Network"
] | null | 483 | 2012.12631 | title_snapshot |
6NFBvWlRXaG | On the Universality of Rotation Equivariant Point Cloud Networks | https://openreview.net/forum?id=6NFBvWlRXaG | [
"Nadav Dym",
"Haggai Maron"
] | Poster | null | Learning functions on point clouds has applications in many fields, including computer vision, computer graphics, physics, and chemistry. Recently, there has been a growing interest in neural architectures that are invariant or equivariant to all three shape-preserving transformations of point clouds: translation, rota... | [
"3D deep learning",
"Rotation invariance",
"Invariant and equivariant deep networks",
"Universal approximation",
"Point clouds"
] | null | 481 | 2010.02449 | title_snapshot |
ATp1nW2FuZL | Neural Learning of One-of-Many Solutions for Combinatorial Problems in Structured Output Spaces | https://openreview.net/forum?id=ATp1nW2FuZL | [
"Yatin Nandwani",
"Deepanshu Jindal",
"Mausam .",
"Parag Singla"
] | Poster | null | Recent research has proposed neural architectures for solving combinatorial problems in structured output spaces. In many such problems, there may exist multiple solutions for a given input, e.g. a partially filled Sudoku puzzle may have many completions satisfying all constraints. Further, we are often interested in f... | [
"Neuro symbolic",
"constraint satisfaction",
"reasoning"
] | null | 478 | 2008.11990 | title_snapshot |
xTJEN-ggl1b | LambdaNetworks: Modeling long-range Interactions without Attention | https://openreview.net/forum?id=xTJEN-ggl1b | [
"Irwan Bello"
] | Spotlight | null | We present lambda layers -- an alternative framework to self-attention -- for capturing long-range interactions between an input and structured contextual information (e.g. a pixel surrounded by other pixels). Lambda layers capture such interactions by transforming available contexts into linear functions, termed lambd... | [
"deep learning",
"neural networks",
"attention",
"transformer",
"vision",
"image classification"
] | null | 476 | 2102.08602 | title_snapshot |
SHvF5xaueVn | GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images | https://openreview.net/forum?id=SHvF5xaueVn | [
"Sungmin Cha",
"Taeeon Park",
"Byeongjoon Kim",
"Jongduk Baek",
"Taesup Moon"
] | Poster | null | We tackle a challenging blind image denoising problem, in which only single distinct noisy images are available for training a denoiser, and no information about noise is known, except for it being zero-mean, additive, and independent of the clean image. In such a setting, which often occurs in practice, it is not poss... | [
"blind denoising",
"unsupervised learning",
"iterative training",
"generative learning"
] | null | 464 | 1905.10488 | title_snapshot |
F2v4aqEL6ze | CPR: Classifier-Projection Regularization for Continual Learning | https://openreview.net/forum?id=F2v4aqEL6ze | [
"Sungmin Cha",
"Hsiang Hsu",
"Taebaek Hwang",
"Flavio Calmon",
"Taesup Moon"
] | Poster | null | We propose a general, yet simple patch that can be applied to existing regularization-based continual learning methods called classifier-projection regularization (CPR). Inspired by both recent results on neural networks with wide local minima and information theory, CPR adds an additional regularization term that maxi... | [
"continual learning",
"regularization",
"wide local minima"
] | null | 463 | 2006.07326 | title_snapshot |
MLSvqIHRidA | Contrastive Divergence Learning is a Time Reversal Adversarial Game | https://openreview.net/forum?id=MLSvqIHRidA | [
"Omer Yair",
"Tomer Michaeli"
] | Spotlight | null | Contrastive divergence (CD) learning is a classical method for fitting unnormalized statistical models to data samples. Despite its wide-spread use, the convergence properties of this algorithm are still not well understood. The main source of difficulty is an unjustified approximation which has been used to derive the... | [
"Unsupervised learning",
"energy based model",
"adversarial learning",
"contrastive divergence",
"noise contrastive estimation"
] | null | 458 | 2012.03295 | title_snapshot |
1OCTOShAmqB | On the Dynamics of Training Attention Models | https://openreview.net/forum?id=1OCTOShAmqB | [
"Haoye Lu",
"Yongyi Mao",
"Amiya Nayak"
] | Poster | null | The attention mechanism has been widely used in deep neural networks as a model component. By now, it has become a critical building block in many state-of-the-art natural language models. Despite its great success established empirically, the working mechanism of attention has not been investigated at a sufficient the... | [] | null | 447 | 2011.10036 | title_snapshot |
OMNB1G5xzd4 | Model-Based Offline Planning | https://openreview.net/forum?id=OMNB1G5xzd4 | [
"Arthur Argenson",
"Gabriel Dulac-Arnold"
] | Poster | null | Offline learning is a key part of making reinforcement learning (RL) useable in real systems. Offline RL looks at scenarios where there is data from a system's operation, but no direct access to the system when learning a policy. Recent work on training RL policies from offline data has shown results both with model-fr... | [
"off-line reinforcement learning",
"model-based reinforcement learning",
"model-based control",
"reinforcement learning",
"model predictive control",
"robotics"
] | null | 445 | 2008.05556 | title_snapshot |
kLbhLJ8OT12 | Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System | https://openreview.net/forum?id=kLbhLJ8OT12 | [
"Jianhong Wang",
"Yuan Zhang",
"Tae-Kyun Kim",
"Yunjie Gu"
] | Poster | null | Designing task-oriented dialogue systems is a challenging research topic, since it needs not only to generate utterances fulfilling user requests but also to guarantee the comprehensibility. Many previous works trained end-to-end (E2E) models with supervised learning (SL), however, the bias in annotated system utteranc... | [
"Task-oriented Dialogue System",
"Natural Language Processing",
"Hierarchical Reinforcement Learning",
"Policy Optimization"
] | null | 443 | 2006.06814 | title_snapshot |
uAX8q61EVRu | Neural Synthesis of Binaural Speech From Mono Audio | https://openreview.net/forum?id=uAX8q61EVRu | [
"Alexander Richard",
"Dejan Markovic",
"Israel D. Gebru",
"Steven Krenn",
"Gladstone Alexander Butler",
"Fernando Torre",
"Yaser Sheikh"
] | Oral | null | We present a neural rendering approach for binaural sound synthesis that can produce realistic and spatially accurate binaural sound in realtime. The network takes, as input, a single-channel audio source and synthesizes, as output, two-channel binaural sound, conditioned on the relative position and orientation of the... | [
"binaural audio",
"sound spatialization",
"neural sound synthesis",
"binaural speech",
"speech processing",
"speech generation"
] | null | 437 | null | null |
NTEz-6wysdb | Distilling Knowledge from Reader to Retriever for Question Answering | https://openreview.net/forum?id=NTEz-6wysdb | [
"Gautier Izacard",
"Edouard Grave"
] | Poster | null | The task of information retrieval is an important component of many natural language processing systems, such as open domain question answering. While traditional methods were based on hand-crafted features, continuous representations based on neural networks recently obtained competitive results. A challenge of using ... | [
"question answering",
"information retrieval"
] | null | 436 | 2012.04584 | title_snapshot |
hr-3PMvDpil | Efficient Certified Defenses Against Patch Attacks on Image Classifiers | https://openreview.net/forum?id=hr-3PMvDpil | [
"Jan Hendrik Metzen",
"Maksym Yatsura"
] | Poster | null | Adversarial patches pose a realistic threat model for physical world attacks on autonomous systems via their perception component. Autonomous systems in safety-critical domains such as automated driving should thus contain a fail-safe fallback component that combines certifiable robustness against patches with efficien... | [
"robustness",
"certified defense",
"adversarial patch",
"aversarial examples"
] | null | 426 | 2102.04154 | title_snapshot |
LwEQnp6CYev | Quantifying Differences in Reward Functions | https://openreview.net/forum?id=LwEQnp6CYev | [
"Adam Gleave",
"Michael D Dennis",
"Shane Legg",
"Stuart Russell",
"Jan Leike"
] | Spotlight | null | For many tasks, the reward function is inaccessible to introspection or too complex to be specified procedurally, and must instead be learned from user data. Prior work has evaluated learned reward functions by evaluating policies optimized for the learned reward. However, this method cannot distinguish between the lea... | [
"rl",
"irl",
"reward learning",
"distance",
"benchmarks"
] | null | 423 | 2006.13900 | title_snapshot |
mSAKhLYLSsl | Dataset Condensation with Gradient Matching | https://openreview.net/forum?id=mSAKhLYLSsl | [
"Bo Zhao",
"Konda Reddy Mopuri",
"Hakan Bilen"
] | Oral | null | As the state-of-the-art machine learning methods in many fields rely on larger datasets, storing datasets and training models on them become significantly more expensive. This paper proposes a training set synthesis technique for data-efficient learning, called Dataset Condensation, that learns to condense large datase... | [
"dataset condensation",
"data-efficient learning",
"image generation"
] | null | 420 | 2006.05929 | title_snapshot |
lU5Rs_wCweN | Taking Notes on the Fly Helps Language Pre-Training | https://openreview.net/forum?id=lU5Rs_wCweN | [
"Qiyu Wu",
"Chen Xing",
"Yatao Li",
"Guolin Ke",
"Di He",
"Tie-Yan Liu"
] | Poster | null | How to make unsupervised language pre-training more efficient and less resource-intensive is an important research direction in NLP. In this paper, we focus on improving the efficiency of language pre-training methods through providing better data utilization. It is well-known that in language data corpus, words follow... | [
"Natural Language Processing",
"Pre-training"
] | null | 416 | 2008.01466 | title_judge |
dlEJsyHGeaL | Graph Edit Networks | https://openreview.net/forum?id=dlEJsyHGeaL | [
"Benjamin Paassen",
"Daniele Grattarola",
"Daniele Zambon",
"Cesare Alippi",
"Barbara Hammer"
] | Poster | null | While graph neural networks have made impressive progress in classification and regression, few approaches to date perform time series prediction on graphs, and those that do are mostly limited to edge changes. We suggest that graph edits are a more natural interface for graph-to-graph learning. In particular, graph e... | [
"graph neural networks",
"graph edit distance",
"time series prediction",
"structured prediction"
] | null | 408 | null | null |
8cpHIfgY4Dj | FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization | https://openreview.net/forum?id=8cpHIfgY4Dj | [
"Lanqing Li",
"Rui Yang",
"Dijun Luo"
] | Poster | null | We study the offline meta-reinforcement learning (OMRL) problem, a paradigm which enables reinforcement learning (RL) algorithms to quickly adapt to unseen tasks without any interactions with the environments, making RL truly practical in many real-world applications. This problem is still not fully understood, for whi... | [
"offline/batch reinforcement learning",
"meta-reinforcement learning",
"multi-task reinforcement learning",
"distance metric learning",
"contrastive learning"
] | null | 405 | 2010.01112 | title_snapshot |
33rtZ4Sjwjn | Effective and Efficient Vote Attack on Capsule Networks | https://openreview.net/forum?id=33rtZ4Sjwjn | [
"Jindong Gu",
"Baoyuan Wu",
"Volker Tresp"
] | Poster | null | Standard Convolutional Neural Networks (CNNs) can be easily fooled by images with small quasi-imperceptible artificial perturbations. As alternatives to CNNs, the recently proposed Capsule Networks (CapsNets) are shown to be more robust to white-box attack than CNNs under popular attack protocols. Besides, the class-co... | [
"Capsule Networks",
"Adversarial Attacks",
"Adversarial Example Detection"
] | null | 402 | 2102.10055 | title_snapshot |
rkQuFUmUOg3 | Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets | https://openreview.net/forum?id=rkQuFUmUOg3 | [
"Hayeon Lee",
"Eunyoung Hyung",
"Sung Ju Hwang"
] | Poster | null | Despite the success of recent Neural Architecture Search (NAS) methods on various tasks which have shown to output networks that largely outperform human-designed networks, conventional NAS methods have mostly tackled the optimization of searching for the network architecture for a single task (dataset), which does not... | [
"Machine Learning",
"Neural Architecture Search",
"Meta-learning"
] | null | 401 | 2107.00860 | title_snapshot |
edJ_HipawCa | Impact of Representation Learning in Linear Bandits | https://openreview.net/forum?id=edJ_HipawCa | [
"Jiaqi Yang",
"Wei Hu",
"Jason D. Lee",
"Simon Shaolei Du"
] | Poster | null | We study how representation learning can improve the efficiency of bandit problems. We study the setting where we play $T$ linear bandits with dimension $d$ concurrently, and these $T$ bandit tasks share a common $k (\ll d)$ dimensional linear representation. For the finite-action setting, we present a new algorithm wh... | [
"linear bandits",
"representation learning",
"multi-task learning"
] | null | 396 | 2010.06531 | title_snapshot |
gwnoVHIES05 | Creative Sketch Generation | https://openreview.net/forum?id=gwnoVHIES05 | [
"Songwei Ge",
"Vedanuj Goswami",
"Larry Zitnick",
"Devi Parikh"
] | Poster | null | Sketching or doodling is a popular creative activity that people engage in. However, most existing work in automatic sketch understanding or generation has focused on sketches that are quite mundane. In this work, we introduce two datasets of creative sketches -- Creative Birds and Creative Creatures -- containing 10k ... | [
"creativity",
"sketches",
"part-based",
"GAN",
"dataset",
"generative art"
] | null | 394 | 2011.10039 | title_snapshot |
068E_JSq9O | Self-supervised Representation Learning with Relative Predictive Coding | https://openreview.net/forum?id=068E_JSq9O | [
"Yao-Hung Hubert Tsai",
"Martin Q. Ma",
"Muqiao Yang",
"Han Zhao",
"Louis-Philippe Morency",
"Ruslan Salakhutdinov"
] | Poster | null | This paper introduces Relative Predictive Coding (RPC), a new contrastive representation learning objective that maintains a good balance among training stability, minibatch size sensitivity, and downstream task performance. The key to the success of RPC is two-fold. First, RPC introduces the relative parameters to reg... | [
"self-supervised learning",
"contrastive learning",
"dependency based method"
] | null | 393 | 2103.11275 | title_snapshot |
uz5uw6gM0m | One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks | https://openreview.net/forum?id=uz5uw6gM0m | [
"Atish Agarwala",
"Abhimanyu Das",
"Brendan Juba",
"Rina Panigrahy",
"Vatsal Sharan",
"Xin Wang",
"Qiuyi Zhang"
] | Poster | null | Can deep learning solve multiple, very different tasks simultaneously? We investigate how the representations of the underlying tasks affect the ability of a single neural network to learn them jointly. We present theoretical and empirical findings that a single neural network is capable of simultaneously learning mult... | [
"deep learning theory",
"multi-task learning"
] | null | 368 | 2103.15261 | title_snapshot |
de11dbHzAMF | Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data | https://openreview.net/forum?id=de11dbHzAMF | [
"Jonathan Pilault",
"Amine El hattami",
"Christopher Pal"
] | Poster | null | Multi-Task Learning (MTL) networks have emerged as a promising method for transferring learned knowledge across different tasks. However, MTL must deal with challenges such as: overfitting to low resource tasks, catastrophic forgetting, and negative task transfer, or learning interference. Often, in Natural Language Pr... | [
"Multi-Task Learning",
"Adaptive Learning",
"Transfer Learning",
"Natural Language Processing",
"Hypernetwork"
] | null | 367 | 2009.09139 | title_snapshot |
04cII6MumYV | A Universal Representation Transformer Layer for Few-Shot Image Classification | https://openreview.net/forum?id=04cII6MumYV | [
"Lu Liu",
"William L. Hamilton",
"Guodong Long",
"Jing Jiang",
"Hugo Larochelle"
] | Poster | null | Few-shot classification aims to recognize unseen classes when presented with only a small number of samples. We consider the problem of multi-domain few-shot image classification, where unseen classes and examples come from diverse data sources. This problem has seen growing interest and has inspired the development of... | [] | null | 365 | 2006.11702 | title_snapshot |
-mWcQVLPSPy | Isometric Propagation Network for Generalized Zero-shot Learning | https://openreview.net/forum?id=-mWcQVLPSPy | [
"Lu Liu",
"Tianyi Zhou",
"Guodong Long",
"Jing Jiang",
"Xuanyi Dong",
"Chengqi Zhang"
] | Poster | null | Zero-shot learning (ZSL) aims to classify images of an unseen class only based on a few attributes describing that class but no access to any training sample. A popular strategy is to learn a mapping between the semantic space of class attributes and the visual space of images based on the seen classes and their data. ... | [
"Zero-shot learning",
"isometric",
"prototype propagation",
"alignment of semantic and visual space"
] | null | 363 | 2102.02038 | title_snapshot |
HajQFbx_yB | Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes | https://openreview.net/forum?id=HajQFbx_yB | [
"Mike Gartrell",
"Insu Han",
"Elvis Dohmatob",
"Jennifer Gillenwater",
"Victor-Emmanuel Brunel"
] | Oral | null | Determinantal point processes (DPPs) have attracted significant attention in machine learning for their ability to model subsets drawn from a large item collection. Recent work shows that nonsymmetric DPP (NDPP) kernels have significant advantages over symmetric kernels in terms of modeling power and predictive perform... | [
"determinantal point processes",
"unsupervised learning",
"representation learning",
"submodular optimization"
] | null | 362 | 2006.09862 | title_snapshot |
37nvvqkCo5 | Long-tail learning via logit adjustment | https://openreview.net/forum?id=37nvvqkCo5 | [
"Aditya Krishna Menon",
"Sadeep Jayasumana",
"Ankit Singh Rawat",
"Himanshu Jain",
"Andreas Veit",
"Sanjiv Kumar"
] | Spotlight | null | Real-world classification problems typically exhibit an imbalanced or long-tailed label distribution, wherein many labels have only a few associated samples. This poses a challenge for generalisation on such labels, and also makes naive learning biased towards dominant labels. In this paper, we present a statistical ... | [
"long-tail learning",
"class imbalance"
] | null | 358 | 2007.07314 | title_snapshot |
S0UdquAnr9k | Locally Free Weight Sharing for Network Width Search | https://openreview.net/forum?id=S0UdquAnr9k | [
"Xiu Su",
"Shan You",
"Tao Huang",
"Fei Wang",
"Chen Qian",
"Changshui Zhang",
"Chang Xu"
] | Spotlight | null | Searching for network width is an effective way to slim deep neural networks with hardware budgets. With this aim, a one-shot supernet is usually leveraged as a performance evaluator to rank the performance \wrt~different width. Nevertheless, current methods mainly follow a manually fixed weight sharing pattern, which ... | [] | null | 346 | 2102.05258 | title_snapshot |
OthEq8I5v1 | Mutual Information State Intrinsic Control | https://openreview.net/forum?id=OthEq8I5v1 | [
"Rui Zhao",
"Yang Gao",
"Pieter Abbeel",
"Volker Tresp",
"Wei Xu"
] | Spotlight | null | Reinforcement learning has been shown to be highly successful at many challenging tasks. However, success heavily relies on well-shaped rewards. Intrinsically motivated RL attempts to remove this constraint by defining an intrinsic reward function. Motivated by the self-consciousness concept in psychology, we make a na... | [
"Intrinsically Motivated Reinforcement Learning",
"Intrinsic Reward",
"Intrinsic Motivation",
"Deep Reinforcement Learning",
"Reinforcement Learning"
] | null | 339 | 2103.08107 | title_snapshot |
WiGQBFuVRv | Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows | https://openreview.net/forum?id=WiGQBFuVRv | [
"Kashif Rasul",
"Abdul-Saboor Sheikh",
"Ingmar Schuster",
"Urs M Bergmann",
"Roland Vollgraf"
] | Spotlight | null | Time series forecasting is often fundamental to scientific and engineering problems and enables decision making. With ever increasing data set sizes, a trivial solution to scale up predictions is to assume independence between interacting time series. However, modeling statistical dependencies can improve accuracy and ... | [
"time series",
"normalizing flows",
"attention",
"probabilistic multivariate forecasting"
] | null | 336 | 2002.06103 | title_snapshot |
iAX0l6Cz8ub | Geometry-aware Instance-reweighted Adversarial Training | https://openreview.net/forum?id=iAX0l6Cz8ub | [
"Jingfeng Zhang",
"Jianing Zhu",
"Gang Niu",
"Bo Han",
"Masashi Sugiyama",
"Mohan Kankanhalli"
] | Oral | null | 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, whether we can keep the accuracy and improve the robustness, is conceptually a... | [
"Adversarial robustness"
] | null | 332 | 2010.01736 | title_snapshot |
IMPnRXEWpvr | Towards Impartial Multi-task Learning | https://openreview.net/forum?id=IMPnRXEWpvr | [
"Liyang Liu",
"Yi Li",
"Zhanghui Kuang",
"Jing-Hao Xue",
"Yimin Chen",
"Wenming Yang",
"Qingmin Liao",
"Wayne Zhang"
] | Poster | null | Multi-task learning (MTL) has been widely used in representation learning. However, naively training all tasks simultaneously may lead to the partial training issue, where specific tasks are trained more adequately than others. In this paper, we propose to learn multiple tasks impartially. Specifically, for the task-sh... | [
"Multi-task Learning",
"Impartial Learning",
"Scene Understanding"
] | null | 331 | null | null |
Uu1Nw-eeTxJ | On Learning Universal Representations Across Languages | https://openreview.net/forum?id=Uu1Nw-eeTxJ | [
"Xiangpeng Wei",
"Rongxiang Weng",
"Yue Hu",
"Luxi Xing",
"Heng Yu",
"Weihua Luo"
] | Poster | null | Recent studies have demonstrated the overwhelming advantage of cross-lingual pre-trained models (PTMs), such as multilingual BERT and XLM, on cross-lingual NLP tasks. However, existing approaches essentially capture the co-occurrence among tokens through involving the masked language model (MLM) objective with token-le... | [
"universal representation learning",
"cross-lingual pretraining",
"hierarchical contrastive learning"
] | null | 330 | 2007.15960 | title_snapshot |
xYGNO86OWDH | Isotropy in the Contextual Embedding Space: Clusters and Manifolds | https://openreview.net/forum?id=xYGNO86OWDH | [
"Xingyu Cai",
"Jiaji Huang",
"Yuchen Bian",
"Kenneth Church"
] | Poster | null | The geometric properties of contextual embedding spaces for deep language models such as BERT and ERNIE, have attracted considerable attention in recent years. Investigations on the contextual embeddings demonstrate a strong anisotropic space such that most of the vectors fall within a narrow cone, leading to high cosi... | [
"Contextual embedding space",
"Isotropy",
"Clusters",
"Manifolds"
] | null | 328 | null | null |
0-EYBhgw80y | MoPro: Webly Supervised Learning with Momentum Prototypes | https://openreview.net/forum?id=0-EYBhgw80y | [
"Junnan Li",
"Caiming Xiong",
"Steven Hoi"
] | Poster | null | We propose a webly-supervised representation learning method that does not suffer from the annotation unscalability of supervised learning, nor the computation unscalability of self-supervised learning. Most existing works on webly-supervised representation learning adopt a vanilla supervised learning method without ac... | [
"webly-supervised learning",
"weakly-supervised learning",
"contrastive learning",
"representation learning"
] | null | 322 | 2009.07995 | title_snapshot |
jLoC4ez43PZ | GraphCodeBERT: Pre-training Code Representations with Data Flow | https://openreview.net/forum?id=jLoC4ez43PZ | [
"Daya Guo",
"Shuo Ren",
"Shuai Lu",
"Zhangyin Feng",
"Duyu Tang",
"Shujie LIU",
"Long Zhou",
"Nan Duan",
"Alexey Svyatkovskiy",
"Shengyu Fu",
"Michele Tufano",
"Shao Kun Deng",
"Colin Clement",
"Dawn Drain",
"Neel Sundaresan",
"Jian Yin",
"Daxin Jiang",
"Ming Zhou"
] | Poster | null | Pre-trained models for programming language have achieved dramatic empirical improvements on a variety of code-related tasks such as code search, code completion, code summarization, etc. However, existing pre-trained models regard a code snippet as a sequence of tokens, while ignoring the inherent structure of code, w... | [
"Pre-training",
"BERT",
"Code Representations",
"Code Structure",
"Data Flow"
] | null | 318 | 2009.08366 | title_snapshot |
HOFxeCutxZR | Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation | https://openreview.net/forum?id=HOFxeCutxZR | [
"Peiye Zhuang",
"Oluwasanmi O Koyejo",
"Alex Schwing"
] | Poster | null | Controllable semantic image editing enables a user to change entire image attributes with a few clicks, e.g., gradually making a summer scene look like it was taken in winter. Classic approaches for this task use a Generative Adversarial Net (GAN) to learn a latent space and suitable latent-space transformations. Howev... | [
"Image manipulation",
"GANs",
"latent space of GANs"
] | null | 315 | 2102.01187 | title_snapshot |
c8P9NQVtmnO | Fourier Neural Operator for Parametric Partial Differential Equations | https://openreview.net/forum?id=c8P9NQVtmnO | [
"Zongyi Li",
"Nikola Borislavov Kovachki",
"Kamyar Azizzadenesheli",
"Burigede liu",
"Kaushik Bhattacharya",
"Andrew Stuart",
"Anima Anandkumar"
] | Poster | null | The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mappin... | [
"Partial differential equation",
"Fourier transform",
"Neural operators"
] | null | 312 | 2010.08895 | title_snapshot |
vYeQQ29Tbvx | Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs | https://openreview.net/forum?id=vYeQQ29Tbvx | [
"Jonathan Frankle",
"David J. Schwab",
"Ari S. Morcos"
] | Poster | null | A wide variety of deep learning techniques from style transfer to multitask learning rely on training affine transformations of features. Most prominent among these is the popular feature normalization technique BatchNorm, which normalizes activations and then subsequently applies a learned affine transform. In this pa... | [
"affine parameters",
"random features",
"batchnorm"
] | null | 310 | 2003.00152 | title_snapshot |
dyaIRud1zXg | Information Laundering for Model Privacy | https://openreview.net/forum?id=dyaIRud1zXg | [
"Xinran Wang",
"Yu Xiang",
"Jun Gao",
"Jie Ding"
] | Spotlight | null | In this work, we propose information laundering, a novel framework for enhancing model privacy. Unlike data privacy that concerns the protection of raw data information, model privacy aims to protect an already-learned model that is to be deployed for public use. The private model can be obtained from general learning ... | [
"Adversarial Attack",
"Machine Learning",
"Model privacy",
"Privacy-utility tradeoff",
"Security"
] | null | 309 | 2009.06112 | title_snapshot |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.