O. Ohrimenko

Artificial Intelligence · 2014–2018

A researcher in the Science corpus: 6 papers; trusts 57 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 133 citations into it; #15,189 of 35,345 by trust here, #16,166 by citations
• Privacy & Security — 45 citations into it; #654 of 709 by trust here, #642 by citations
• Information Systems — 33 citations into it; #3,861 of 8,771 by trust here, #3,876 by citations
• Safety, Robustness & Interpretability — 32 citations into it; #1,390 of 2,251 by trust here, #856 by citations
• Signal Processing — 24 citations into it
• Computer Vision and Pattern Recognition — 23 citations into it
• Computational Theory and Mathematics — 22 citations into it
• Computer Networks and Communications — 18 citations into it

Whom O. Ohrimenko trusts overall

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

1. Rafail M. Ostrovsky
2. Oded Goldreich
3. Emil Stefanov
4. Elaine Shi
5. Felix Schuster
6. Bernhard Schölkopf
7. Alex Smola
8. Benny Pinkas
9. Yoon Kim
10. Jason Aaron Edward Weston

Who trusts O. Ohrimenko overall

1. Ú. Erlingsson
2. Hung Dang
3. Florian Tramèr
4. Felix Schuster
5. Fatemehsadat Mireshghallah
6. Tianwei Zhang
7. Zecheng He
8. Fan Mo
9. Congzheng Song
10. Andrea Bittau

Papers from O. Ohrimenko's perspective

Ordered by how strongly the researchers O. Ohrimenko 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. 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. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Contamination Attacks and Mitigation in Multi-Party Machine Learning (2018) — 76 citations
• An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors (2018) — 35 citations
• Forward and Backward Private Searchable Encryption from Constrained Cryptographic Primitives (2017) — 435 citations
• Oblivious Multi-Party Machine Learning on Trusted Processors (2016) — 602 citations
• Observing and Preventing Leakage in MapReduce (2015) — 92 citations
• The Melbourne Shuffle: Improving Oblivious Storage in the Cloud (2014) — 77 citations