Ching-yao Chan

Reinforcement Learning · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 10 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 9 citations into it
• Artificial Intelligence — 7 citations into it; #8,831 of 35,345 by trust here, #23,731 by citations

Whom Ching-yao Chan trusts overall

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

1. Pin Wang
2. Ahmad El Sallab
3. Dylan Hadfield-Menell
4. Richard S. Sutton
5. Volodymyr Mnih

Who trusts Ching-yao Chan overall

1. Szilárd Aradi
2. Edouard Leurent
3. Victor Talpaert
4. Praveen Palanisamy
5. B. R. Kiran
6. Ibrahim Sobh
7. Pin Wang
8. Laurene Claussmann
9. Sampo Kuutti
10. Patrick Mannion

Papers from Ching-yao Chan's perspective

Ordered by how strongly the researchers Ching-yao Chan trusts endorse each paper. Same corpus, a different order for every researcher.

1. Long Short-Term Memory
2. Human-level control through deep reinforcement learning
3. Policy Gradient Methods for Reinforcement Learning with Function Approximation
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Gradient-based learning applied to document recognition
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. ImageNet classification with deep convolutional neural networks
8. Learning Multiple Layers of Features from Tiny Images
9. Attention is All you Need (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers (2018) — 270 citations
• Formulation of deep reinforcement learning architecture toward autonomous driving for on-ramp merge (2017) — 124 citations