Ray Jiang

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 5 papers; trusts 24 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 107 citations into it
• Safety, Robustness & Interpretability — 56 citations into it; #2,126 of 2,251 by trust here, #957 by citations
• Reinforcement Learning — 39 citations into it
• Safety Research — 33 citations into it
• AI Ethics, Fairness & Society — 23 citations into it
• Sociology and Political Science — 20 citations into it
• Optimization & Training — 11 citations into it
• Applied Mathematics — 6 citations into it

Whom Ray Jiang trusts overall

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

1. Silvia Chiappa
2. Faisal Kamiran
3. Michael Feldman
4. Toon Calders
5. Harikrishna Narasimhan
6. Marco Cuturi
7. Moritz Hardt
8. Andrew Cotter
9. Geoffrey E. Hinton
10. J. Urgen Schmidhuber

Who trusts Ray Jiang overall

1. Silvia Chiappa
2. Luca Oneto
3. Ruibo Liu
4. Aldo Pacchiano
5. Ioana Baldini
6. Charan Reddy
7. Po-Sen Huang
8. Paula Czarnowska
9. Chenyan Jia

Papers from Ray Jiang's perspective

Ordered by how strongly the researchers Ray Jiang 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Causally Correct Partial Models for Reinforcement Learning (2020) — 36 citations
• Reducing Sentiment Bias in Language Models via Counterfactual Evaluation (2019) — 251 citations
• Degenerate Feedback Loops in Recommender Systems (2019) — 247 citations
• A General Approach to Fairness with Optimal Transport (2020) — 97 citations
• Wasserstein Fair Classification (2019) — 216 citations