Zongzhang Zhang

Reinforcement Learning · 2017–2022

A researcher in the Science corpus: 4 papers; trusts 49 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 70 citations into it
• Artificial Intelligence — 61 citations into it; #13,889 of 35,345 by trust here, #23,731 by citations
• Computer Vision and Pattern Recognition — 8 citations into it
• Computational Theory and Mathematics — 7 citations into it; #4,216 of 12,182 by trust here, #6,463 by citations
• Representation & Self-Supervised Learning — 6 citations into it

Whom Zongzhang Zhang trusts overall

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

1. Richard S. Sutton
2. Hado van Hasselt
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Pieter Abbeel
6. Yoshua Bengio
7. Yann LeCun
8. Sergey Levine
9. Andrew Barto
10. Kaiming He

Who trusts Zongzhang Zhang overall

1. Min He
2. Che Wang
3. Yan Zheng
4. Qingfeng Lan
5. Hongliang Guo
6. Jianye Hao
7. Zijian Zhou
8. Yangchen Pan

Papers from Zongzhang Zhang's perspective

Ordered by how strongly the researchers Zongzhang Zhang 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Adapt to Environment Sudden Changes by Learning a Context Sensitive Policy (2022) — 45 citations
• Weighted Double Q-learning (2017) — 121 citations
• Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments (2018) — 38 citations
• Hierarchical Deep Multiagent Reinforcement Learning (2018) — 24 citations