Che Wang

Reinforcement Learning · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 129 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 92 citations into it
• Artificial Intelligence — 81 citations into it; #15,346 of 35,345 by trust here, #24,417 by citations
• Management Science and Operations Research — 22 citations into it
• Computational Theory and Mathematics — 17 citations into it; #5,851 of 12,182 by trust here, #10,901 by citations
• Electrical and Electronic Engineering — 14 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Control and Systems Engineering — 12 citations into it
• Optimization & Training — 10 citations into it

Whom Che Wang trusts overall

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

1. Richard S. Sutton
2. Tuomas Haarnoja
3. Hado van Hasselt
4. Longxin Lin
5. Sergey Levine
6. E. Todorov
7. Geoffrey E. Hinton
8. David Silver
9. Timothy P. Lillicrap
10. Diederik P. Kingma

Who trusts Che Wang overall

1. Samarth Sinha
2. Youngmin Oh
3. Nicklas Hansen
4. Philip J. Ball
5. Jiaming Song
6. Jinwoo Shin
7. Hao Su
8. Laura M. Smith
9. Christos Kaplanis
10. Animesh Garg

Papers from Che Wang's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Reinforcement Learning: An Introduction
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning (2019) — 143 citations
• Randomized Ensembled Double Q-Learning: Learning Fast Without a Model (2021) — 397 citations
• Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the Past (2019) — 54 citations