Kaiqing Zhang

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 166 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 195 citations into it; #12,392 of 35,345 by trust here, #12,387 by citations
• Reinforcement Learning — 166 citations into it
• Management Science and Operations Research — 71 citations into it; #7,072 of 11,946 by trust here, #4,921 by citations
• Computer Networks and Communications — 53 citations into it; #5,997 of 9,886 by trust here, #3,638 by citations
• Computational Theory and Mathematics — 45 citations into it; #7,748 of 12,182 by trust here, #3,796 by citations
• Game & Decision Theory — 41 citations into it
• Optimization & Training — 32 citations into it
• Economics and Econometrics — 31 citations into it

Whom Kaiqing Zhang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Richard S. Sutton
2. Michael L. Littman
3. David Silver
4. R´emi Munos
5. Michael H. Bowling
6. Ronald J. Williams
7. Vivek Shripad Borkar
8. Csaba Szepesvari
9. Geoffrey E. Hinton
10. Johannes Heinrich

Who trusts Kaiqing Zhang overall

1. Jieyi Long
2. Sven Gronauer
3. Afshin Oroojlooyjadid
4. A. Perera
5. Amal Feriani
6. Donghwan Lee
7. Yan Zhang
8. Hadi Nekoei
9. Zhuoran Yang
10. Heechang Ryu

Papers from Kaiqing Zhang's perspective

Ordered by how strongly the researchers Kaiqing Zhang trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Reinforcement Learning: An Introduction
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms (2019) — 1,692 citations
• Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents (2018) — 685 citations
• A Multi-Agent Off-Policy Actor-Critic Algorithm for Distributed Reinforcement Learning (2019) — 72 citations
• Near-Optimal Model-Free Reinforcement Learning in Non-Stationary Episodic MDPs (2021) — 49 citations