James R. Foulds

Artificial Intelligence · 2008–2018

A researcher in the Science corpus: 5 papers; trusts 56 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 36 citations into it; #6,805 of 35,345 by trust here, #14,181 by citations
• Safety, Robustness & Interpretability — 26 citations into it
• Safety Research — 23 citations into it; #1,994 of 5,829 by trust here, #4,390 by citations
• Sociology and Political Science — 14 citations into it; #8,994 of 22,756 by trust here, #18,468 by citations
• AI Ethics, Fairness & Society — 13 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it; #5,075 of 21,575 by trust here, #10,154 by citations
• Privacy & Security — 11 citations into it
• Statistical Learning Theory — 9 citations into it

Whom James R. Foulds trusts overall

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

1. Sally A. Goldman
2. Cynthia Dwork
3. Zhi-Hua Zhou
4. Ron Kohavi
5. Min-Ling Zhang
6. Jun Wang
7. Thomas G. Dietterich
8. J. Ross Quinlan
9. Stuart Andrews
10. Yixin Chen

Who trusts James R. Foulds overall

1. Yu-Xiang Wang
2. Jaume Amores
3. Garrett Bernstein
4. Zhi-Hua Zhou
5. Tomás Pevný
6. Angelina Wang
7. Veronika Cheplygina
8. Andrej Karpathy
9. Joseph Geumlek
10. Qiang Zhang

Papers from James R. Foulds's perspective

Ordered by how strongly the researchers James R. Foulds 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A review of multi-instance learning assumptions (2010) — 420 citations
• Revisiting Multiple-Instance Learning Via Embedded Instance Selection (2008) — 147 citations
• An Intersectional Definition of Fairness (2018) — 231 citations
• On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis (2016) — 104 citations
• Bayesian Modeling of Intersectional Fairness: The Variance of Bias (2018) — 47 citations