Kun Shao

Reinforcement Learning · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 82 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 130 citations into it
• Artificial Intelligence — 125 citations into it
• Computational Theory and Mathematics — 17 citations into it
• Electrical and Electronic Engineering — 17 citations into it
• Game & Decision Theory — 10 citations into it
• Model Architectures — 9 citations into it
• Economics and Econometrics — 8 citations into it
• Sociology and Political Science — 8 citations into it

Whom Kun Shao trusts overall

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

1. Richard S. Sutton
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Volodymyr Mnih
5. Alex Graves
6. J. Urgen Schmidhuber
7. Alex Krizhevsky
8. Sergey Levine
9. Diederik P. Kingma
10. Sepp Hochreiter

Who trusts Kun Shao overall

1. Sanmit Narvekar
2. Maciej Wolczyk
3. Bei Peng
4. Zhentao Tang

Papers from Kun Shao's perspective

Ordered by how strongly the researchers Kun Shao 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• StarCraft Micromanagement With Reinforcement Learning and Curriculum Transfer Learning (2018) — 188 citations
• A Survey of Deep Reinforcement Learning in Video Games (2019) — 237 citations
• Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning (2020) — 236 citations