Omar Fawzi

Artificial Intelligence · 2014–2019

A researcher in the Science corpus: 9 papers; trusts 178 researchers and is trusted by 108 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 104 citations into it; #2,936 of 35,345 by trust here, #2,514 by citations
• Safety, Robustness & Interpretability — 63 citations into it; #156 of 2,251 by trust here, #161 by citations
• Computer Vision and Pattern Recognition — 58 citations into it; #3,815 of 21,575 by trust here, #2,456 by citations
• Model Architectures — 14 citations into it
• Signal Processing — 13 citations into it
• Atomic and Molecular Physics, and Optics — 12 citations into it; #4,072 of 7,135 by trust here, #5,903 by citations
• Computational Theory and Mathematics — 10 citations into it
• Optimization & Training — 9 citations into it

Whom Omar Fawzi trusts overall

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

1. Renato Renner
2. Marco Tomamichel
3. Matthias Christandl
4. N. Datta
5. Christian Szegedy
6. Alex Krizhevsky
7. Karen Simonyan
8. Geoffrey E. Hinton
9. Alhussein Fawzi
10. Diederik P. Kingma

Who trusts Omar Fawzi overall

1. Alhussein Fawzi
2. Seyed-Mohsen Moosavi-Dezfooli
3. Florian Tramèr
4. Naveed Akhtar
5. Ahmed Almheiri
6. Chunchuan Lyu
7. Dimitris Tsipras
8. Nicholas Carlini
9. A. Serban
10. Jiajun Lu

Papers from Omar Fawzi's perspective

Ordered by how strongly the researchers Omar Fawzi 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Universal Adversarial Perturbations (2016) — 2,828 citations
• Adversarial vulnerability for any classifier (2018) — 273 citations
• Analysis of classifiers’ robustness to adversarial perturbations (2015) — 386 citations
• Analysis of universal adversarial perturbations (2017) — 122 citations
• Learning dynamic polynomial proofs (2019) — 18 citations
• Robustness of classifiers to uniform $\ell_p$ and Gaussian noise (2018) — 21 citations
• Analysis of classifiers’ robustness to adversarial perturbations (2017)
• Fundamental limits on adversarial robustness (2015) — 58 citations
• Quantum Conditional Mutual Information and Approximate Markov Chains (2014) — 332 citations