Shun Liao

Reinforcement Learning · 2017

A researcher in the Science corpus: 1 paper; trusts 14 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 19 citations into it; #1,304 of 2,242 by trust here, #1,004 by citations
• Artificial Intelligence — 18 citations into it; #17,735 of 35,345 by trust here, #10,798 by citations
• Optimization & Training — 12 citations into it; #1,559 of 2,601 by trust here, #1,009 by citations

Whom Shun Liao trusts overall

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

1. James Martens
2. Ronald J. Williams
3. Shun-ichi Amari
4. Diederik P. Kingma
5. Richard S. Sutton
6. N. Schraudolph
7. Sham M. Kakade
8. John Schulman
9. Geoffrey E. Hinton

Who trusts Shun Liao overall

1. Guodong Zhang
2. Shansi Zhang
3. T. T. Gomes

Papers from Shun Liao's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Asynchronous Methods for Deep Reinforcement Learning
5. Human-level control through deep reinforcement learning
6. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
7. Gradient-based learning applied to document recognition
8. Trust Region Policy Optimization (International Conference on Machine Learning)
9. Long Short-Term Memory
10. Continuous control with deep reinforcement learning (International Conference on Learning Representations)

Their papers

• Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (2017) — 674 citations