Qing Wang

Reinforcement Learning · with Liu · 2018

A researcher in the Science corpus: 3 papers; trusts 32 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 74 citations into it
• Artificial Intelligence — 60 citations into it
• Management Science and Operations Research — 13 citations into it
• Optimization & Training — 11 citations into it

Whom Qing Wang trusts overall

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

1. Richard S. Sutton
2. John Schulman
3. Sham M. Kakade
4. Philip S. Thomas
5. Andrew Barto
6. Volodymyr Mnih
7. Ronald J. Williams
8. Geoffrey E. Hinton
9. David Silver
10. Sergey Levine

Who trusts Qing Wang overall

1. Scott Fujimoto
2. Haotian Fu
3. Ashvin Nair
4. Rasool Fakoor
5. David Brandfonbrener
6. Zijian Zhou
7. Shixiang Shane Gu
8. Ziyun Wang
9. Deheng Ye
10. Hongyao Tang

Papers from Qing Wang's perspective

Ordered by how strongly the researchers Qing Wang 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. 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

• TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game (2018) — 78 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