Tian Han

Computer Vision and Pattern Recognition · with Wu · 2016–2020

A researcher in the Science corpus: 6 papers; trusts 606 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 174 citations into it; #6,261 of 21,575 by trust here, #7,584 by citations
• Generative Models — 135 citations into it; #561 of 1,956 by trust here, #1,010 by citations
• Artificial Intelligence — 114 citations into it; #6,699 of 35,345 by trust here, #9,275 by citations
• Probabilistic & Bayesian Methods — 76 citations into it; #1,079 of 1,525 by trust here, #809 by citations
• Signal Processing — 33 citations into it
• Optimization & Training — 22 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Statistical and Nonlinear Physics — 19 citations into it; #2,840 of 10,127 by trust here, #5,011 by citations

Whom Tian Han trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Song-Chun Zhu
4. Yoshua Bengio
5. Radford M. Neal
6. Alex Krizhevsky
7. Yann LeCun
8. Ilya Sutskever
9. Kaiming He
10. Ruslan Salakhutdinov

Who trusts Tian Han overall

1. Jianwen Xie
2. Erik Nijkamp
3. Song-Chun Zhu
4. Ying Nian Wu
5. Zhisheng Xiao
6. Ruiqi Gao
7. Jyoti Aneja
8. Matthew D. Hoffman
9. Mitch Hill
10. Arash Vahdat

Papers from Tian Han's perspective

Ordered by how strongly the researchers Tian Han 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• On the Anatomy of MCMC-based Maximum Likelihood Learning of Energy-Based Models (2019) — 185 citations
• Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model (2018) — 74 citations
• Learning Latent Space Energy-Based Prior Model (2020) — 146 citations
• Alternating Back-Propagation for Generator Network (2016) — 139 citations
• Joint Training of Variational Auto-Encoder and Latent Energy-Based Model (2020) — 63 citations
• Learning Generator Networks for Dynamic Patterns (2019) — 10 citations