Hassan Takabi

Artificial Intelligence · 2010–2018

A researcher in the Science corpus: 4 papers; trusts 29 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #17,891 of 35,345 by trust here, #10,942 by citations
• Privacy & Security — 24 citations into it; #411 of 709 by trust here, #404 by citations
• Safety, Robustness & Interpretability — 15 citations into it
• Computational Theory and Mathematics — 9 citations into it
• Computer Networks and Communications — 6 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it; #12,876 of 21,575 by trust here, #11,950 by citations
• ML Systems & Hardware — 5 citations into it

Whom Hassan Takabi trusts overall

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

1. Shai Halevi
2. Craig Gentry
3. Roi Livni
4. Nathan Dowlin
5. Victor Shoup
6. Kristin E. Lauter
7. Thore Graepel
8. Louis J. M. Aslett
9. Raphael Bost
10. Shai Shalev-Shwartz

Who trusts Hassan Takabi overall

1. Ehsan Hesamifard
2. M. Sadegh Riazi
3. Mohammed Abdullatif Alzain
4. Yaochen Hu
5. Jinhyun So
6. Shanhe Yi
7. Rawad Bitar
8. Abhishek Singh
9. Georgios Kaissis
10. Eric Pardede

Papers from Hassan Takabi's perspective

Ordered by how strongly the researchers Hassan Takabi trusts endorse each paper. Same corpus, a different order for every researcher.

1. CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy
2. ImageNet classification with deep convolutional neural networks
3. On the Computational Efficiency of Training Neural Networks
4. Gradient-based learning applied to document recognition
5. Deep Learning with Differential Privacy
6. Learning Multiple Layers of Features from Tiny Images
7. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
8. (Leveled) Fully Homomorphic Encryption without Bootstrapping
9. ML Confidential: Machine Learning on Encrypted Data
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Privacy-preserving Machine Learning as a Service (2018) — 275 citations
• CryptoDL: Deep Neural Networks over Encrypted Data (2017) — 414 citations
• Security and Privacy Challenges in Cloud Computing Environments (2010) — 1,453 citations
• Preserving Multi-party Machine Learning with Homomorphic Encryption (2016)