Zhitang Chen

Artificial Intelligence · with Ng · 2019

A researcher in the Science corpus: 2 papers; trusts 80 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #19,211 of 35,345 by trust here, #23,731 by citations
• Causal Inference & Discovery — 27 citations into it; #293 of 364 by trust here, #327 by citations
• Probabilistic & Bayesian Methods — 16 citations into it
• Statistics and Probability — 12 citations into it
• Computational Theory and Mathematics — 11 citations into it; #9,596 of 12,182 by trust here, #8,294 by citations
• Reinforcement Learning — 9 citations into it; #1,885 of 2,242 by trust here, #2,036 by citations
• Computer Vision and Pattern Recognition — 8 citations into it
• Graph Learning — 8 citations into it

Whom Zhitang Chen trusts overall

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

1. Jan Peters
2. Kun Zhang
3. Diederik P. Kingma
4. Tom L. Burr
5. Bernhard Schölkopf
6. Ronald J. Williams
7. David Maxwell Chickering
8. Dominik Janzing
9. Geoffrey E. Hinton
10. Aapo Hyvärinen

Who trusts Zhitang Chen overall

1. Matthew James Vowels

Papers from Zhitang Chen's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Causal Discovery with Reinforcement Learning (2019) — 293 citations
• Masked Gradient-Based Causal Structure Learning (2019) — 143 citations