Hadi Salman

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 225 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 156 citations into it; #13,598 of 35,345 by trust here, #9,275 by citations
• Computer Vision and Pattern Recognition — 95 citations into it; #11,659 of 21,575 by trust here, #12,172 by citations
• Safety, Robustness & Interpretability — 88 citations into it
• Optimization & Training — 24 citations into it
• Scaling & Learning Theory — 24 citations into it; #258 of 428 by trust here, #351 by citations
• Electrical and Electronic Engineering — 18 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Statistical Learning Theory — 17 citations into it; #718 of 1,038 by trust here, #817 by citations

Whom Hadi Salman trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Ian J. Goodfellow
6. Christian Szegedy
7. Diederik P. Kingma
8. Yann LeCun
9. Ilya Sutskever
10. Ross B. Girshick

Who trusts Hadi Salman overall

1. Huan Zhang
2. Eric Wong
3. Greg Yang
4. Kaidi Xu
5. Zeyuan Allen-Zhu
6. Leslie Rice
7. Klas Leino
8. Sungyoon Lee
9. Amirreza Shaeiri
10. Eugenia Iofinova

Papers from Hadi Salman's perspective

Ordered by how strongly the researchers Hadi Salman 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. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers (2019) — 640 citations
• A Fine-Grained Spectral Perspective on Neural Networks (2019) — 122 citations
• A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks (2019) — 299 citations
• Do Adversarially Robust ImageNet Models Transfer Better? (2020) — 492 citations