Hongyao Tang

Reinforcement Learning · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 32 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 83 citations into it
• Artificial Intelligence — 81 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Game & Decision Theory — 11 citations into it

Whom Hongyao Tang trusts overall

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

1. Geoffrey E. Hinton
2. Richard S. Sutton
3. J. Urgen Schmidhuber
4. Michael L. Littman
5. Yoshua Bengio
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Kaiming He
9. Pieter Abbeel
10. Peter S. Dayan

Who trusts Hongyao Tang overall

1. Sanjeevan Ahilan
2. Haotian Fu
3. Peter S. Dayan

Papers from Hongyao Tang's perspective

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

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

Their papers

• Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning (2020) — 61 citations
• Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces (2019) — 84 citations
• Q-value Path Decomposition for Deep Multiagent Reinforcement Learning (2020) — 65 citations
• Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning (2020) — 236 citations
• Hierarchical Deep Multiagent Reinforcement Learning (2018) — 24 citations