Mohammad Yaghini

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 19 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it; #27,000 of 35,345 by trust here, #28,481 by citations
• Safety, Robustness & Interpretability — 22 citations into it
• Safety Research — 13 citations into it; #3,870 of 5,829 by trust here, #4,390 by citations
• AI Ethics, Fairness & Society — 10 citations into it; #367 of 494 by trust here, #432 by citations
• Sociology and Political Science — 10 citations into it
• Privacy & Security — 9 citations into it

Whom Mohammad Yaghini trusts overall

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

1. Cynthia Dwork
2. Frank P. Kelly
3. Reza Shokri
4. Moritz Hardt
5. Toon Calders
6. Muhammad Bilal Zafar
7. Milad Nasr
8. Tianqi Chen
9. Payman Mohassel
10. Alexandre Sablayrolles

Who trusts Mohammad Yaghini overall

1. Lingxiao Huang
2. Ninareh Mehrabi
3. Eugene Bagdasarian

Papers from Mohammad Yaghini's perspective

Ordered by how strongly the researchers Mohammad Yaghini 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. Gradient-based learning applied to document recognition
4. Learning Multiple Layers of Features from Tiny Images
5. ImageNet: A large-scale hierarchical image database
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Attention is All you Need (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Non-Discriminatory Machine Learning through Convex Fairness Criteria (2018) — 103 citations
• Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning (2019) — 41 citations