NeurIPS
Collection
Accepted papers for NeurIPS (Conference on Neural Information Processing Systems), one dataset per year. • 13 items • Updated
title stringlengths 14 148 | paper_url stringlengths 105 105 | authors listlengths 0 21 | type stringclasses 0
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values | abstract large_stringlengths 388 2k | keywords listlengths 0 0 | TL;DR large_stringclasses 0
values | submission_number int64 1 1.43k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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Compositional Plan Vectors | https://proceedings.neurips.cc/paper_files/paper/2019/hash/00989c20ff1386dc386d8124ebcba1a5-Abstract.html | [
"Coline Devin",
"Daniel Geng",
"Pieter Abbeel",
"Trevor Darrell",
"Sergey Levine"
] | null | null | Autonomous agents situated in real-world environments must be able to master large repertoires of skills. While a single short skill can be learned quickly, it would be impractical to learn every task independently. Instead, the agent should share knowledge across behaviors such that each task can be learned efficientl... | [] | null | 1 | 1910.14033 | title_judge | [
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Learning to Propagate for Graph Meta-Learning | https://proceedings.neurips.cc/paper_files/paper/2019/hash/00ac8ed3b4327bdd4ebbebcb2ba10a00-Abstract.html | [
"LU LIU",
"Tianyi Zhou",
"Guodong Long",
"Jing Jiang",
"Chengqi Zhang"
] | null | null | Meta-learning extracts the common knowledge from learning different tasks and uses it for unseen tasks. It can significantly improve tasks that suffer from insufficient training data, e.g., few-shot learning. In most meta-learning methods, tasks are implicitly related by sharing parameters or optimizer. In this paper, we... | [] | null | 2 | 1909.05024 | title_snapshot | [
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XNAS: Neural Architecture Search with Expert Advice | https://proceedings.neurips.cc/paper_files/paper/2019/hash/00e26af6ac3b1c1c49d7c3d79c60d000-Abstract.html | [
"Niv Nayman",
"Asaf Noy",
"Tal Ridnik",
"Itamar Friedman",
"Rong Jin",
"Lihi Zelnik"
] | null | null | This paper introduces a novel optimization method for differential neural architecture search, based on the theory of prediction with expert advice. Its optimization criterion is well fitted for an architecture-selection, i.e., it minimizes the regret incurred by a sub-optimal selection of operations. Unlike previous s... | [] | null | 3 | 1906.08031 | title_snapshot | [
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Multi-resolution Multi-task Gaussian Processes | https://proceedings.neurips.cc/paper_files/paper/2019/hash/0118a063b4aae95277f0bc1752c75abf-Abstract.html | [
"Oliver Hamelijnck",
"Theodoros Damoulas",
"Kangrui Wang",
"Mark Girolami"
] | null | null | We consider evidence integration from potentially dependent observation processes under varying spatio-temporal sampling resolutions and noise levels. We offer a multi-resolution multi-task (MRGP) framework that allows for both inter-task and intra-task multi-resolution and multi-fidelity. We develop shallow Gaussian P... | [] | null | 4 | 1906.08344 | title_snapshot | [
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Deep Equilibrium Models | https://proceedings.neurips.cc/paper_files/paper/2019/hash/01386bd6d8e091c2ab4c7c7de644d37b-Abstract.html | [
"Shaojie Bai",
"J. Zico Kolter",
"Vladlen Koltun"
] | null | null | We present a new approach to modeling sequential data: the deep equilibrium model (DEQ). Motivated by an observation that the hidden layers of many existing deep sequence models converge towards some fixed point, we propose the DEQ approach that directly finds these equilibrium points via root-finding. Such a method is... | [] | null | 5 | 1909.01377 | title_snapshot | [
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Cross Attention Network for Few-shot Classification | https://proceedings.neurips.cc/paper_files/paper/2019/hash/01894d6f048493d2cacde3c579c315a3-Abstract.html | [
"Ruibing Hou",
"Hong Chang",
"Bingpeng MA",
"Shiguang Shan",
"Xilin Chen"
] | null | null | Few-shot classification aims to recognize unlabeled samples from unseen classes given only few labeled samples. The unseen classes and low-data problem make few-shot classification very challenging. Many existing approaches extracted features from labeled and unlabeled samples independently, as a result, the features a... | [] | null | 6 | 1910.07677 | title_snapshot | [
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Order Optimal One-Shot Distributed Learning | https://proceedings.neurips.cc/paper_files/paper/2019/hash/018b59ce1fd616d874afad0f44ba338d-Abstract.html | [
"Arsalan Sharifnassab",
"Saber Salehkaleybar",
"S. Jamaloddin Golestani"
] | null | null | We consider distributed statistical optimization in one-shot setting, where there are $m$ machines each observing $n$ i.i.d samples. Based on its observed samples, each machine then sends an $O(\log(mn))$-length message to a server, at which a parameter minimizing an expected loss is to be estimated. We propose an algo... | [] | null | 7 | 1911.00731 | title_snapshot | [
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Exact Gaussian Processes on a Million Data Points | https://proceedings.neurips.cc/paper_files/paper/2019/hash/01ce84968c6969bdd5d51c5eeaa3946a-Abstract.html | [
"Ke Wang",
"Geoff Pleiss",
"Jacob Gardner",
"Stephen Tyree",
"Kilian Q. Weinberger",
"Andrew Gordon Wilson"
] | null | null | Gaussian processes (GPs) are flexible non-parametric models, with a capacity that grows with the available data. However, computational constraints with standard inference procedures have limited exact GPs to problems with fewer than about ten thousand training points, necessitating approximations for larger datasets. ... | [] | null | 8 | 1903.08114 | title_snapshot | [
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Asymmetric Valleys: Beyond Sharp and Flat Local Minima | https://proceedings.neurips.cc/paper_files/paper/2019/hash/01d8bae291b1e4724443375634ccfa0e-Abstract.html | [
"Haowei He",
"Gao Huang",
"Yang Yuan"
] | null | null | Despite the non-convex nature of their loss functions, deep neural networks are known to generalize well when optimized with stochastic gradient descent (SGD). Recent work conjectures that SGD with proper configuration is able to find wide and flat local minima, which are correlated with good generalization performance. I... | [] | null | 9 | 1902.00744 | title_snapshot | [
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Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization | https://proceedings.neurips.cc/paper_files/paper/2019/hash/021e1ea77bd91aaa0fc4d01a943a654e-Abstract.html | [
"Viet Anh Nguyen",
"Soroosh Shafieezadeh-Abadeh",
"Man-Chung Yue",
"Daniel Huhn",
"Wolfram Wiesemann"
] | null | null | A fundamental problem arising in many areas of machine learning is the evaluation of the likelihood of a given observation under different nominal distributions. Frequently, these nominal distributions are themselves estimated from data, which makes them susceptible to estimation errors. We thus propose to replace each... | [] | null | 10 | 1910.07817 | title_snapshot | [
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Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging | https://proceedings.neurips.cc/paper_files/paper/2019/hash/0224cd598e48c5041c7947fd5cb20d53-Abstract.html | [
"Pooria Joulani",
"András György",
"Csaba Szepesvari"
] | null | null | We present two new algorithms, ASYNCADA and HEDGEHOG, for asynchronous sparse online and stochastic optimization. ASYNCADA is, to our knowledge, the first asynchronous stochastic optimization algorithm with finite-time data-dependent convergence guarantees for generic convex constraints. In addition, ASYNCADA: (a) allo... | [] | null | 11 | null | null | [
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