Jiming Liu

Artificial Intelligence · with Yu · 2007–2019

A researcher in the Science corpus: 5 papers; trusts 189 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 145 citations into it; #13,665 of 35,345 by trust here, #20,836 by citations
• Reinforcement Learning — 100 citations into it
• Statistics and Probability — 41 citations into it; #12,061 of 12,932 by trust here, #10,855 by citations
• Medical & Health AI — 37 citations into it
• Epidemiology — 32 citations into it; #381 of 2,148 by trust here, #1,722 by citations
• Computer Vision and Pattern Recognition — 29 citations into it
• Endocrinology, Diabetes and Metabolism — 29 citations into it
• Molecular Biology — 27 citations into it

Whom Jiming Liu trusts overall

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

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

Who trusts Jiming Liu overall

1. Chao Yu
2. Guoqi Ren
3. Ruosong Wang
4. X. Shen
5. Quan Zheng
6. Shamim Nemati

Papers from Jiming Liu's perspective

Ordered by how strongly the researchers Jiming Liu 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
• 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
• Community Mining from Signed Social Networks (2007) — 400 citations