Heng Dong

Artificial Intelligence · 2020

A researcher in the Science corpus: 2 papers; trusts 51 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 92 citations into it; #18,696 of 35,345 by trust here, #19,018 by citations
• Reinforcement Learning — 74 citations into it; #1,693 of 2,242 by trust here, #1,864 by citations
• Computational Theory and Mathematics — 18 citations into it
• Management Science and Operations Research — 18 citations into it
• Computer Networks and Communications — 16 citations into it
• LLM Agents & Tool Use — 12 citations into it
• Electrical and Electronic Engineering — 6 citations into it
• Game & Decision Theory — 6 citations into it

Whom Heng Dong trusts overall

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

1. Richard S. Sutton
2. Jakob N. Foerster
3. Tabish Rashid
4. Frans Adriaan Oliehoek
5. Geoffrey E. Hinton
6. Andrew Barto
7. Yoshua Bengio
8. J. Diamond
9. Diederik P. Kingma
10. Mikayel Samvelyan

Who trusts Heng Dong overall

1. Tonghan Wang
2. Filippos Christianos
3. Jiechuan Jiang
4. Kyunghwan Son
5. Yihan Wang

Papers from Heng Dong's perspective

Ordered by how strongly the researchers Heng Dong 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. 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

• Off-Policy Multi-Agent Decomposed Policy Gradients (2020) — 220 citations
• ROMA: Multi-Agent Reinforcement Learning with Emergent Roles (2020) — 304 citations