Han Liu

Reinforcement Learning · with Zhang · 2016–2018

A researcher in the Science corpus: 7 papers; trusts 74 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 146 citations into it
• Artificial Intelligence — 127 citations into it; #29,314 of 35,345 by trust here, #13,163 by citations
• Management Science and Operations Research — 24 citations into it
• Computational Theory and Mathematics — 23 citations into it
• Computer Networks and Communications — 23 citations into it
• Optimization & Training — 23 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it; #6,831 of 21,575 by trust here, #8,502 by citations
• Game & Decision Theory — 8 citations into it

Whom Han Liu trusts overall

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

1. Richard S. Sutton
2. Michael L. Littman
3. Yann LeCun
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Andrew Barto
7. Yoshua Bengio
8. John Schulman
9. Sham M. Kakade
10. Volodymyr Mnih

Who trusts Han Liu overall

1. Zixuan Ke
2. Bing Liu
3. Gyuhak Kim
4. Sven Gronauer
5. Fei Ye
6. Donghwan Lee

Papers from Han Liu's perspective

Ordered by how strongly the researchers Han Liu 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Reinforcement Learning: An Introduction
9. GENERATIVE ADVERSARIAL NETS
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Continual Learning in Generative Adversarial Nets (2017) — 135 citations
• TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game (2018) — 78 citations
• Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents (2018) — 685 citations
• Factorized Q-learning for large-scale multi-agent systems (2018) — 75 citations
• Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space (2018) — 240 citations
• Exponentially Weighted Imitation Learning for Batched Historical Data (2018) — 143 citations
• Blind Attacks on Machine Learners (2016) — 6 citations