Chun-Hao Kingsley Chang

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 89 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #31,728 of 35,345 by trust here, #19,018 by citations
• Computer Vision and Pattern Recognition — 26 citations into it; #19,544 of 21,575 by trust here, #13,269 by citations
• Safety, Robustness & Interpretability — 16 citations into it
• Epidemiology — 9 citations into it
• Generative Models — 9 citations into it
• Probabilistic & Bayesian Methods — 7 citations into it
• Medical & Health AI — 5 citations into it
• Reinforcement Learning — 5 citations into it

Whom Chun-Hao Kingsley Chang trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yarin Gal
4. Ronald J. Williams
5. Alex Krizhevsky
6. Yoshua Bengio
7. Karen Simonyan
8. Jimmy Ba
9. Ilya Sutskever
10. Richard S. Sutton

Who trusts Chun-Hao Kingsley Chang overall

1. Javier Antorán Cabiscol
2. Yash Goyal
3. Chao Yu
4. Ian Covert
5. Chih-Kuan Yeh
6. Javier Antorán
7. Matthew R. O'Shaughnessy
8. Jörg Wagner
9. Umang Bhatt
10. Xuhong Li

Papers from Chun-Hao Kingsley Chang's perspective

Ordered by how strongly the researchers Chun-Hao Kingsley Chang 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Explaining Image Classifiers by Counterfactual Generation (2018) — 298 citations
• Dropout Feature Ranking for Deep Learning Models (2017) — 53 citations
• Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RL (2018) — 5 citations