Haoran Tang

Reinforcement Learning · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 74 researchers and is trusted by 174 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 92 citations into it; #332 of 2,242 by trust here, #362 by citations
• Artificial Intelligence — 83 citations into it; #3,123 of 35,345 by trust here, #5,638 by citations
• Computer Vision and Pattern Recognition — 21 citations into it; #2,843 of 21,575 by trust here, #4,372 by citations
• Management Science and Operations Research — 20 citations into it
• Control and Systems Engineering — 13 citations into it
• Optimization & Training — 12 citations into it
• Computational Theory and Mathematics — 11 citations into it
• Computer Networks and Communications — 8 citations into it

Whom Haoran Tang trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. E. Todorov
4. John Schulman
5. Ronald J. Williams
6. Yoshua Bengio
7. Diederik P. Kingma
8. Sergey Levine
9. J. Urgen Schmidhuber
10. Volodymyr Mnih

Who trusts Haoran Tang overall

1. Sergey Levine
2. Yilun Du
3. Ofir Nachum
4. Justin Fu
5. Pieter Abbeel
6. Tuomas Haarnoja
7. Bo Dai
8. Junhyuk Oh
9. Abhishek Gupta
10. Chelsea Finn

Papers from Haoran Tang's perspective

Ordered by how strongly the researchers Haoran 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Reinforcement Learning with Deep Energy-Based Policies (2017) — 1,623 citations
• Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning? (2019) — 122 citations
• #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning (2016) — 866 citations