NeurIPS 2019

1,428 papers

Spherical Text Embedding

Yu Meng, Jiaxin Huang, Guangyuan Wang, Chao Zhang, Honglei Zhuang

Hyperbolic Graph Convolutional Neural Networks

Ines Chami, Zhitao Ying, Christopher Re, Jure Leskovec

How degenerate is the parametrization of neural networks with the ReLU activation function?

Dennis Maximilian Elbrächter, Julius Berner, Philipp Grohs

Explicit Explore-Exploit Algorithms in Continuous State Spaces

Mikael Henaff

Random Projections and Sampling Algorithms for Clustering of High-Dimensional Polygonal Curves

Stefan Meintrup, Alexander Munteanu, Dennis Rohde

Flattening a Hierarchical Clustering through Active Learning

Fabio Vitale, Anand Rajagopalan, Claudio Gentile

Cormorant: Covariant Molecular Neural Networks

Brandon Anderson, Truong Son Hy, Risi Kondor

Learning Erdos-Renyi Random Graphs via Edge Detecting Queries

Zihan Li, Matthias Fresacher, Jonathan Scarlett

Learning Mean-Field Games

Xin Guo, Anran Hu, Renyuan Xu, Junzi Zhang

DTWNet: a Dynamic Time Warping Network

Xingyu Cai, Tingyang Xu, Jinfeng Yi, Junzhou Huang, Sanguthevar Rajasekaran

Generalization of Reinforcement Learners with Working and Episodic Memory

Meire Fortunato, Melissa Tan, Ryan Faulkner, Steven Hansen, Adrià Puigdomènech Badia

A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution

Qing Qu, Xiao Li, Zhihui Zhu

Lower Bounds on Adversarial Robustness from Optimal Transport

Arjun Nitin Bhagoji, Daniel Cullina, Prateek Mittal

Hyperspherical Prototype Networks

Pascal Mettes, Elise van der Pol, Cees Snoek

Zero-Shot Semantic Segmentation

Maxime Bucher, Tuan-Hung VU, Matthieu Cord, Patrick Pérez

A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport

Arun Jambulapati, Aaron Sidford, Kevin Tian

Improved Precision and Recall Metric for Assessing Generative Models

Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, Timo Aila

Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging

Pooria Joulani, András György, Csaba Szepesvári

Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization

Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Man-Chung Yue, Daniel Huhn, Wolfram Wiesemann

Asymmetric Valleys: Beyond Sharp and Flat Local Minima

Haowei He, Gao Huang, Yang Yuan

Exact Gaussian Processes on a Million Data Points

Ke Wang, Geoff Pleiss, Jacob Gardner, Stephen Tyree, Kilian Q. Weinberger

Order Optimal One-Shot Distributed Learning

Arsalan Sharifnassab, Saber Salehkaleybar, S. Jamaloddin Golestani

Cross Attention Network for Few-shot Classification

Ruibing Hou, Hong Chang, Bingpeng MA, Shiguang Shan, Xilin Chen

Deep Equilibrium Models

Shaojie Bai, J. Zico Kolter, Vladlen Koltun

Multi-resolution Multi-task Gaussian Processes

Oliver Hamelijnck, Theodoros Damoulas, Kangrui Wang, Mark Girolami

XNAS: Neural Architecture Search with Expert Advice

Niv Nayman, Asaf Noy, Tal Ridnik, Itamar Friedman, Rong Jin

Learning to Propagate for Graph Meta-Learning

LU LIU, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang

Compositional Plan Vectors

Coline Devin, Daniel Geng, Pieter Abbeel, Trevor Darrell, Sergey Levine