Zheng Qu
Artificial Intelligence · 2014–2019
A researcher in the Science corpus: 7 papers; trusts 77 researchers and is trusted by 55 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 133 citations into it; #2,955 of 35,345 by trust here, #6,517 by citations
- Optimization & Training — 115 citations into it; #454 of 2,601 by trust here, #725 by citations
- Computational Mechanics — 107 citations into it; #470 of 10,001 by trust here, #769 by citations
- Statistics and Probability — 40 citations into it; #1,473 of 12,932 by trust here, #2,579 by citations
- Mathematical Optimization — 26 citations into it; #249 of 422 by trust here, #256 by citations
- Computational Theory and Mathematics — 25 citations into it; #3,939 of 12,182 by trust here, #3,924 by citations
- Numerical Analysis — 14 citations into it
- Computer Vision and Pattern Recognition — 11 citations into it; #5,219 of 21,575 by trust here, #11,286 by citations
Whom Zheng Qu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yurii E. Nesterov
- Peter Richtárik
- Tong Zhang
- Shai Shalev-Shwartz
- Martin Takác
- Julien Mairal
- Lin Xiao
- Mark W. Schmidt
- Rie Johnson
- Francis R. Bach
Who trusts Zheng Qu overall
- Peter Richtárik
- Jakub Konecný
- Nicolas Loizou
- Dominik Csiba
- Samuel Horváth
- Filip Hanzely
- Robert Mansel Gower
- Martin Takác
- Xun Qian
- Chenxin Ma
Papers from Zheng Qu's perspective
Ordered by how strongly the researchers Zheng Qu trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Fast distributed coordinate descent for non-strongly convex losses (2014) — 59 citations
- Coordinate descent with arbitrary sampling I: algorithms and complexity† (2014) — 135 citations
- Stochastic Dual Coordinate Ascent with Adaptive Probabilities (2015) — 98 citations
- SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization (2015) — 105 citations
- L-SVRG and L-Katyusha with Arbitrary Sampling (2019) — 34 citations
- Semi-stochastic coordinate descent (2014) — 94 citations
- Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling (2015) — 93 citations