Seyed-Mohsen Moosavi-Dezfooli

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 8 papers; trusts 272 researchers and is trusted by 919 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 107 citations into it; #853 of 35,345 by trust here, #1,229 by citations
• Safety, Robustness & Interpretability — 76 citations into it; #40 of 2,251 by trust here, #73 by citations
• Computer Vision and Pattern Recognition — 67 citations into it; #1,462 of 21,575 by trust here, #1,091 by citations
• Model Architectures — 25 citations into it
• Signal Processing — 21 citations into it
• Optimization & Training — 16 citations into it
• Natural Language Processing — 13 citations into it
• Biophysics — 8 citations into it

Whom Seyed-Mohsen Moosavi-Dezfooli trusts overall

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

1. Alex Krizhevsky
2. Christian Szegedy
3. Alhussein Fawzi
4. Geoffrey E. Hinton
5. Ian J. Goodfellow
6. Kaiming He
7. Karen Simonyan
8. Ilya Sutskever
9. Yann LeCun
10. Yoshua Bengio

Who trusts Seyed-Mohsen Moosavi-Dezfooli overall

1. Nicholas Carlini
2. Naveed Akhtar
3. Matthias Hein
4. Alhussein Fawzi
5. Shumeet Baluja
6. Danilo Vasconcellos Vargas
7. Nina Narodytska
8. Florian Tramèr
9. Arjun Nitin Bhagoji
10. Avishek Joey Bose

Papers from Seyed-Mohsen Moosavi-Dezfooli's perspective

Ordered by how strongly the researchers Seyed-Mohsen Moosavi-Dezfooli 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions

Their papers

• DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks (2015) — 5,469 citations
• Universal Adversarial Perturbations (2016) — 2,828 citations
• Robustness of classifiers: from adversarial to random noise (2016) — 405 citations
• Geometric Robustness of Deep Networks: Analysis and Improvement (2017) — 140 citations
• Analysis of universal adversarial perturbations (2017) — 122 citations
• Empirical Study of the Topology and Geometry of Deep Networks (2018) — 157 citations
• Robustness via Curvature Regularization, and Vice Versa (2018) — 345 citations
• Universal Adversarial Attacks on Text Classifiers (2019) — 110 citations