Ehsan Hesamifard

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 29 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #17,903 of 35,345 by trust here, #15,004 by citations
• Privacy & Security — 24 citations into it; #412 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; #12,878 of 21,575 by trust here, #11,950 by citations
• ML Systems & Hardware — 5 citations into it

Whom Ehsan Hesamifard trusts overall

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

1. Craig Gentry
2. Shai Halevi
3. Payman Mohassel
4. Thore Graepel
5. Nathan Dowlin
6. Louis J. M. Aslett
7. Hassan Takabi
8. Kristin E. Lauter
9. Raphael Bost
10. Bita Darvish Rouhani

Who trusts Ehsan Hesamifard overall

1. M. Sadegh Riazi
2. Jinhyun So
3. Abhishek Singh
4. Georgios Kaissis
5. Nitin Agrawal
6. Fatemehsadat Mirshghallah
7. Edward Chou
8. Ho Bae
9. Praneeth Vepakomma
10. Brandon Reagen

Papers from Ehsan Hesamifard's perspective

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

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

Their papers

• Privacy-preserving Machine Learning as a Service (2018) — 275 citations
• CryptoDL: Deep Neural Networks over Encrypted Data (2017) — 414 citations
• Preserving Multi-party Machine Learning with Homomorphic Encryption (2016)