Zheng Tian

Artificial Intelligence · with Anthony · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 21 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it; #12,052 of 35,345 by trust here, #13,163 by citations
• Reinforcement Learning — 32 citations into it
• Management Science and Operations Research — 10 citations into it
• Optimization & Training — 7 citations into it
• Economics and Econometrics — 6 citations into it; #7,128 of 23,481 by trust here, #7,262 by citations
• Computer Vision and Pattern Recognition — 5 citations into it

Whom Zheng Tian trusts overall

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

1. Ronald J. Williams
2. Michael L. Littman
3. Diederik P. Kingma
4. Stéphane Ross
5. Richard S. Sutton
6. David Silver
7. Geoffrey E. Hinton
8. Yoshua Bengio
9. Jimmy Ba
10. Alex Krizhevsky

Who trusts Zheng Tian overall

1. Eric Zelikman
2. James Goodman
3. Hector Geffner
4. Iddo Drori
5. Sebastian Schulze
6. Yuhuai Wu
7. Paul Christiano
8. Ching-An Cheng
9. Wen Sun
10. Ariel Keselman

Papers from Zheng Tian's perspective

Ordered by how strongly the researchers Zheng Tian 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning

Their papers

• Thinking Fast and Slow with Deep Learning and Tree Search (2017) — 481 citations
• Learning to Communicate Implicitly by Actions (2018) — 35 citations