NeurIPS
Collection
Accepted papers for NeurIPS (Conference on Neural Information Processing Systems), one dataset per year. • 13 items • Updated
title stringlengths 13 150 | paper_url stringlengths 105 105 | authors listlengths 1 31 | type stringclasses 0
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values | abstract large_stringlengths 145 2k | keywords listlengths 0 0 | TL;DR large_stringclasses 0
values | submission_number int64 1 1.9k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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A graph similarity for deep learning | https://proceedings.neurips.cc/paper_files/paper/2020/hash/0004d0b59e19461ff126e3a08a814c33-Abstract.html | [
"Seongmin Ok"
] | null | null | Graph neural networks (GNNs) have been successful in learning representations from graphs. Many popular GNNs follow the pattern of aggregate-transform: they aggregate the neighbors' attributes and then transform the results of aggregation with a learnable function. Analyses of these GNNs explain which pairs of non-iden... | [] | null | 1 | null | null | [
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An Unsupervised Information-Theoretic Perceptual Quality Metric | https://proceedings.neurips.cc/paper_files/paper/2020/hash/00482b9bed15a272730fcb590ffebddd-Abstract.html | [
"Sangnie Bhardwaj",
"Ian Fischer",
"Johannes Ballé",
"Troy Chinen"
] | null | null | Tractable models of human perception have proved to be challenging to build. Hand-designed models such as MS-SSIM remain popular predictors of human image quality judgements due to their simplicity and speed. Recent modern deep learning approaches can perform better, but they rely on supervised data which can be costly... | [] | null | 2 | 2006.06752 | title_snapshot | [
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Self-Supervised MultiModal Versatile Networks | https://proceedings.neurips.cc/paper_files/paper/2020/hash/0060ef47b12160b9198302ebdb144dcf-Abstract.html | [
"Jean-Baptiste Alayrac",
"Adria Recasens",
"Rosalia Schneider",
"Relja Arandjelović",
"Jason Ramapuram",
"Jeffrey De Fauw",
"Lucas Smaira",
"Sander Dieleman",
"Andrew Zisserman"
] | null | null | Videos are a rich source of multi-modal supervision. In this work, we learn representations using self-supervision by leveraging three modalities naturally present in videos: visual, audio and language streams. To this end, we introduce the notion of a multimodal versatile network -- a network that can ingest multiple ... | [] | null | 3 | 2006.16228 | title_snapshot | [
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Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method | https://proceedings.neurips.cc/paper_files/paper/2020/hash/007ff380ee5ac49ffc34442f5c2a2b86-Abstract.html | [
"Simiao Ren",
"Willie Padilla",
"Jordan Malof"
] | null | null | We consider the task of solving generic inverse problems, where one wishes to determine the hidden parameters of a natural system that will give rise to a particular set of measurements. Recently many new approaches based upon deep learning have arisen, generating promising results. We conceptualize these models as dif... | [] | null | 4 | 2009.12919 | title_snapshot | [
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Off-Policy Evaluation and Learning for External Validity under a Covariate Shift | https://proceedings.neurips.cc/paper_files/paper/2020/hash/0084ae4bc24c0795d1e6a4f58444d39b-Abstract.html | [
"Masatoshi Uehara",
"Masahiro Kato",
"Shota Yasui"
] | null | null | We consider the evaluation and training of a new policy for the evaluation data by using the historical data obtained from a different policy. The goal of off-policy evaluation (OPE) is to estimate the expected reward of a new policy over the evaluation data, and that of off-policy learning (OPL) is to find a new polic... | [] | null | 5 | 2002.11642 | title_snapshot | [
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Neural Methods for Point-wise Dependency Estimation | https://proceedings.neurips.cc/paper_files/paper/2020/hash/00a03ec6533ca7f5c644d198d815329c-Abstract.html | [
"Yao-Hung Hubert Tsai",
"Han Zhao",
"Makoto Yamada",
"Louis-Philippe Morency",
"Ruslan Salakhutdinov"
] | null | null | Since its inception, the neural estimation of mutual information (MI) has demonstrated the empirical success of modeling expected dependency between high-dimensional random variables. However, MI is an aggregate statistic and cannot be used to measure point-wise dependency between different events. In this work, instea... | [] | null | 6 | 2006.05553 | title_snapshot | [
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Fast and Flexible Temporal Point Processes with Triangular Maps | https://proceedings.neurips.cc/paper_files/paper/2020/hash/00ac8ed3b4327bdd4ebbebcb2ba10a00-Abstract.html | [
"Oleksandr Shchur",
"Nicholas Gao",
"Marin Biloš",
"Stephan Günnemann"
] | null | null | Temporal point process (TPP) models combined with recurrent neural networks provide a powerful framework for modeling continuous-time event data. While such models are flexible, they are inherently sequential and therefore cannot benefit from the parallelism of modern hardware. By exploiting the recent developments in ... | [] | null | 7 | 2006.12631 | title_snapshot | [
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Backpropagating Linearly Improves Transferability of Adversarial Examples | https://proceedings.neurips.cc/paper_files/paper/2020/hash/00e26af6ac3b1c1c49d7c3d79c60d000-Abstract.html | [
"Yiwen Guo",
"Qizhang Li",
"Hao Chen"
] | null | null | The vulnerability of deep neural networks (DNNs) to adversarial examples has drawn great attention from the community. In this paper, we study the transferability of such examples, which lays the foundation of many black-box attacks on DNNs. We revisit a not so new but definitely noteworthy hypothesis of Goodfellow et ... | [] | null | 8 | 2012.03528 | title_snapshot | [
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PyGlove: Symbolic Programming for Automated Machine Learning | https://proceedings.neurips.cc/paper_files/paper/2020/hash/012a91467f210472fab4e11359bbfef6-Abstract.html | [
"Daiyi Peng",
"Xuanyi Dong",
"Esteban Real",
"Mingxing Tan",
"Yifeng Lu",
"Gabriel Bender",
"Hanxiao Liu",
"Adam Kraft",
"Chen Liang",
"Quoc V. Le"
] | null | null | Neural networks are sensitive to hyper-parameter and architecture choices. Automated Machine Learning (AutoML) is a promising paradigm for automating these choices. Current ML software libraries, however, are quite limited in handling the dynamic interactions among the components of AutoML. For example, efficient NAS a... | [] | null | 9 | 2101.08809 | title_snapshot | [
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Fourier Sparse Leverage Scores and Approximate Kernel Learning | https://proceedings.neurips.cc/paper_files/paper/2020/hash/012d9fe15b2493f21902cd55603382ec-Abstract.html | [
"Tamas Erdelyi",
"Cameron Musco",
"Christopher Musco"
] | null | null | We prove new explicit upper bounds on the leverage scores of Fourier sparse functions under both the Gaussian and Laplace measures. In particular, we study s-sparse functions of the form $f(x) = \sum_{j=1}^s a_j e^{i \lambda_j x}$ for coefficients $a_j \in C$ and frequencies $\lambda_j \in R$. Bounding Fourier sparse l... | [] | null | 10 | 2006.07340 | title_snapshot | [
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Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds | https://proceedings.neurips.cc/paper_files/paper/2020/hash/0163cceb20f5ca7b313419c068abd9dc-Abstract.html | [
"Nicholas Harvey",
"Christopher Liaw",
"Tasuku Soma"
] | null | null | We consider the problem of nonnegative submodular maximization in the online setting. At time step t, an algorithm selects a set St ∈ C ⊆ 2^V where C is a feasible family of sets. An adversary then reveals a submodular function ft. The goal is to design an efficient algorithm for minimizing the expected approximate reg... | [] | null | 11 | null | null | [
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