Chao Yu

Artificial Intelligence · with Ren · 2015–2020

A researcher in the Science corpus: 9 papers; trusts 212 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 180 citations into it; #13,301 of 35,345 by trust here, #15,542 by citations
• Reinforcement Learning — 146 citations into it
• Medical & Health AI — 45 citations into it
• Statistics and Probability — 42 citations into it; #12,128 of 12,932 by trust here, #10,855 by citations
• Epidemiology — 38 citations into it; #413 of 2,148 by trust here, #1,722 by citations
• Control and Systems Engineering — 37 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it
• Management Science and Operations Research — 31 citations into it

Whom Chao Yu trusts overall

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

1. Richard S. Sutton
2. Andrew Y. Ng
3. Andrew Barto
4. Susan A. Murphy
5. Geoffrey E. Hinton
6. Eric B. Laber
7. Pieter Abbeel
8. Sonali Parbhoo
9. Damien Ernst
10. Brett L. Moore

Who trusts Chao Yu overall

1. Jiming Liu
2. Guoqi Ren
3. Shamim Nemati
4. Ruosong Wang
5. Y. Liu
6. Dean Phillips Foster
7. Yinzhao Dong
8. Yujing Hu
9. Sham M. Kakade
10. Thanh Thi Nguyen

Papers from Chao Yu's perspective

Ordered by how strongly the researchers Chao Yu 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. Reinforcement Learning: An Introduction
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep Inverse Reinforcement Learning for Sepsis Treatment (2019) — 49 citations
• Reinforcement Learning in Healthcare: A Survey (2019) — 788 citations
• Emotional Multiagent Reinforcement Learning in Spatial Social Dilemmas (2015) — 72 citations
• Supervised-actor-critic reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units (2020) — 40 citations
• Distributed Multiagent Coordinated Learning for Autonomous Driving in Highways Based on Dynamic Coordination Graphs (2020) — 125 citations
• Inverse reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units (2019) — 61 citations
• Incorporating causal factors into reinforcement learning for dynamic treatment regimes in HIV (2019) — 34 citations
• Adaptively Shaping Reinforcement Learning Agents via Human Reward (2018) — 6 citations
• Multiagent Learning of Coordination in Loosely Coupled Multiagent Systems (2015) — 46 citations