Lei Han

Artificial Intelligence · with Liu · 2014–2019

A researcher in the Science corpus: 9 papers; trusts 143 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 113 citations into it; #7,847 of 35,345 by trust here, #11,327 by citations
• Reinforcement Learning — 94 citations into it; #2,020 of 2,242 by trust here, #1,545 by citations
• Representation & Self-Supervised Learning — 40 citations into it; #2,018 of 3,145 by trust here, #2,624 by citations
• Optimization & Training — 36 citations into it
• Computer Vision and Pattern Recognition — 32 citations into it
• Computational Mechanics — 24 citations into it; #1,801 of 10,001 by trust here, #2,407 by citations
• Management Science and Operations Research — 17 citations into it
• Electrical and Electronic Engineering — 15 citations into it

Whom Lei Han trusts overall

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

1. R. Caruana
2. Robert Tibshirani
3. Jieping Ye
4. Alon Zweig
5. Jianhui Chen
6. Richard S. Sutton
7. Geoffrey E. Hinton
8. Jun Liu
9. Yu Zhang
10. Yoshua Bengio

Who trusts Lei Han overall

1. Yu Zhang
2. Qiang Yang
3. Yu Zhang
4. Jingwen Ye
5. Jianhang Zhou
6. Tabish Rashid
7. Yutao Huang
8. Garrett Wilson
9. Sebastian Ruder
10. Ying Wei

Papers from Lei Han's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game (2018) — 78 citations
• Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space (2018) — 240 citations
• LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning (2019) — 194 citations
• Exponentially Weighted Imitation Learning for Batched Historical Data (2018) — 143 citations
• Temporal Causal Inference with Time Lag (2018) — 15 citations
• Multi-Stage Multi-Task Learning with Reduced Rank (2016) — 66 citations
• Learning Tree Structure in Multi-Task Learning (2015) — 77 citations
• Learning Multi-Level Task Groups in Multi-Task Learning (2015) — 78 citations
• Encoding Tree Sparsity in Multi-Task Learning: A Probabilistic Framework (2014) — 43 citations