Ali Shahin Shamsabadi

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 239 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 166 citations into it; #13,713 of 35,345 by trust here, #12,083 by citations
• Computer Vision and Pattern Recognition — 77 citations into it
• Privacy & Security — 62 citations into it
• Safety, Robustness & Interpretability — 40 citations into it
• Optimization & Training — 24 citations into it
• Sociology and Political Science — 19 citations into it; #18,908 of 22,756 by trust here, #12,387 by citations
• Computational Theory and Mathematics — 17 citations into it
• Representation & Self-Supervised Learning — 17 citations into it

Whom Ali Shahin Shamsabadi trusts overall

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

1. Cynthia Dwork
2. Diederik P. Kingma
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Yann LeCun
6. Kaiming He
7. Alex Krizhevsky
8. Christian Szegedy
9. Ilya Sutskever
10. Sergey Ioffe

Who trusts Ali Shahin Shamsabadi overall

1. Fatemehsadat Mireshghallah
2. Mohammadkazem Taram
3. Fatemehsadat Mirshghallah
4. Abhishek Singh
5. Fan Mo
6. Hadi Esmaeilzadeh
7. Hamed Haddadi
8. Zhicong Huang
9. Praneeth Vepakomma

Papers from Ali Shahin Shamsabadi's perspective

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

Their papers

• QUOTIENT: Two-Party Secure Neural Network Training and Prediction (2019) — 245 citations
• Deep Private-Feature Extraction (2018) — 107 citations
• DarkneTZ: towards model privacy at the edge using trusted execution environments (2020) — 240 citations
• PrivEdge: From Local to Distributed Private Training and Prediction (2020) — 21 citations
• A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics (2017) — 266 citations