Mahmood Sharif

Safety, Robustness & Interpretability · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 226 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Safety, Robustness & Interpretability — 87 citations into it; #647 of 2,251 by trust here, #445 by citations
• Artificial Intelligence — 79 citations into it; #9,554 of 35,345 by trust here, #5,600 by citations
• Computer Vision and Pattern Recognition — 59 citations into it; #7,468 of 21,575 by trust here, #4,402 by citations
• Signal Processing — 27 citations into it; #2,013 of 7,572 by trust here, #603 by citations
• Hardware and Architecture — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Molecular Biology — 6 citations into it
• Optimization & Training — 6 citations into it

Whom Mahmood Sharif trusts overall

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

1. Ian J. Goodfellow
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Christian Szegedy
5. Nicholas Carlini
6. Nicolas Papernot
7. Andrea Vedaldi
8. Diederik P. Kingma
9. Yoshua Bengio
10. Yann LeCun

Who trusts Mahmood Sharif overall

1. Florian Tramèr
2. Logan Engstrom
3. Edward Chou
4. Dimitris Tsipras
5. Ivan Evtimov
6. Nicholas Carlini
7. Danilo Vasconcellos Vargas
8. Nicolas Papernot
9. Hossein Hosseini
10. Eric Wong

Papers from Mahmood Sharif's perspective

Ordered by how strongly the researchers Mahmood Sharif 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition (2016) — 1,803 citations
• On the Suitability of Lp-Norms for Creating and Preventing Adversarial Examples (2018) — 146 citations
• Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition (2018) — 99 citations