Zijian Zhou

Reinforcement Learning · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 69 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 68 citations into it
• Artificial Intelligence — 62 citations into it; #27,634 of 35,345 by trust here, #26,682 by citations
• Management Science and Operations Research — 19 citations into it
• Computational Theory and Mathematics — 13 citations into it
• Electrical and Electronic Engineering — 11 citations into it
• Control and Systems Engineering — 10 citations into it
• Optimization & Training — 6 citations into it

Whom Zijian Zhou trusts overall

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

1. Scott Fujimoto
2. Richard S. Sutton
3. Sergey Levine
4. Tuomas Haarnoja
5. Diederik P. Kingma
6. Geoffrey E. Hinton
7. Hado van Hasselt
8. Longxin Lin
9. E. Todorov
10. Pieter Abbeel

Who trusts Zijian Zhou overall

1. Scott Fujimoto
2. David Brandfonbrener
3. Nicklas Hansen
4. Shixiang Shane Gu
5. Ziyun Wang
6. Philip J. Ball

Papers from Zijian Zhou's perspective

Ordered by how strongly the researchers Zijian Zhou 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. Reinforcement Learning: An Introduction
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. GENERATIVE ADVERSARIAL NETS

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