Tonghan Wang

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 6 papers; trusts 156 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 256 citations into it; #8,796 of 35,345 by trust here, #11,921 by citations
• Reinforcement Learning — 200 citations into it; #1,160 of 2,242 by trust here, #1,395 by citations
• Management Science and Operations Research — 50 citations into it
• Computer Networks and Communications — 39 citations into it; #9,371 of 9,886 by trust here, #6,788 by citations
• Computational Theory and Mathematics — 37 citations into it; #8,782 of 12,182 by trust here, #5,239 by citations
• LLM Agents & Tool Use — 28 citations into it
• Computer Vision and Pattern Recognition — 23 citations into it; #16,329 of 21,575 by trust here, #13,815 by citations
• Sociology and Political Science — 21 citations into it; #21,103 of 22,756 by trust here, #15,556 by citations

Whom Tonghan Wang trusts overall

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

1. Jakob N. Foerster
2. Richard S. Sutton
3. Tabish Rashid
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Chongjie Zhang
8. Gregory Farquhar
9. Alex Krizhevsky
10. J. Urgen Schmidhuber

Who trusts Tonghan Wang overall

1. Jiechuan Jiang
2. Yihan Wang
3. Chao Yu
4. Jianhao Wang
5. Chongjie Zhang
6. Beining Han
7. Filippos Christianos
8. Zongqing Lu
9. Tarun Gupta
10. Bei Peng

Papers from Tonghan Wang's perspective

Ordered by how strongly the researchers Tonghan 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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

• Celebrating Diversity in Shared Multi-Agent Reinforcement Learning (2021) — 208 citations
• RODE: Learning Roles to Decompose Multi-Agent Tasks (2020) — 286 citations
• Influence-Based Multi-Agent Exploration (2019) — 160 citations
• Off-Policy Multi-Agent Decomposed Policy Gradients (2020) — 220 citations
• Learning Nearly Decomposable Value Functions Via Communication Minimization (2019) — 179 citations
• ROMA: Multi-Agent Reinforcement Learning with Emergent Roles (2020) — 304 citations