Heinrich Jiang

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 7 papers; trusts 118 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 104 citations into it; #13,699 of 35,345 by trust here, #15,236 by citations
• Safety, Robustness & Interpretability — 55 citations into it; #748 of 2,251 by trust here, #1,290 by citations
• Safety Research — 46 citations into it; #1,496 of 5,829 by trust here, #2,344 by citations
• AI Ethics, Fairness & Society — 36 citations into it; #163 of 494 by trust here, #371 by citations
• Optimization & Training — 30 citations into it
• Computer Vision and Pattern Recognition — 25 citations into it
• Sociology and Political Science — 17 citations into it
• Management Science and Operations Research — 16 citations into it

Whom Heinrich Jiang trusts overall

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

1. Andrew Cotter
2. Yann LeCun
3. Faisal Kamiran
4. Alex Krizhevsky
5. Geoffrey E. Hinton
6. Karen Simonyan
7. Harikrishna Narasimhan
8. Moritz Hardt
9. Alekh Agarwal
10. Christian Szegedy

Who trusts Heinrich Jiang overall

1. Sercan Ö. Arik
2. Yuji Roh
3. Tomas Pfister
4. Angelina Wang
5. Arnaud Van Looveren
6. Luca Oneto
7. Vikash Sehwag
8. Terrance Devries
9. Dallas Card
10. Zhen Lin

Papers from Heinrich Jiang's perspective

Ordered by how strongly the researchers Heinrich Jiang 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals (2018) — 191 citations
• Identifying and Correcting Label Bias in Machine Learning (2019) — 332 citations
• Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints (2018) — 119 citations
• To Trust Or Not To Trust A Classifier (2018) — 524 citations
• A General Approach to Fairness with Optimal Transport (2020) — 97 citations
• Wasserstein Fair Classification (2019) — 216 citations
• Two-Player Games for Efficient Non-Convex Constrained Optimization (2018) — 137 citations