Vasyl Pihur

Artificial Intelligence · with Erlingsson · 2014–2018

A researcher in the Science corpus: 3 papers; trusts 13 researchers and is trusted by 124 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #1,849 of 35,345 by trust here, #3,582 by citations
• Privacy & Security — 41 citations into it; #32 of 709 by trust here, #60 by citations
• Sociology and Political Science — 16 citations into it
• Statistics and Probability — 10 citations into it; #3,269 of 12,932 by trust here, #3,330 by citations
• Computer Science Applications — 7 citations into it; #4 of 1,855 by trust here, #25 by citations
• Safety, Robustness & Interpretability — 7 citations into it; #132 of 2,251 by trust here, #324 by citations
• Optimization & Training — 5 citations into it

Whom Vasyl Pihur trusts overall

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

1. Cynthia Dwork
2. Ilya Mironov
3. Ú. Erlingsson
4. Frank McSherry
5. Eric R. Ziegel
6. Aleksandra Korolova
7. Mark Bun
8. Yann LeCun
9. H. Brendan McMahan
10. Kobbi Nissim

Who trusts Vasyl Pihur overall

1. Tianhao Wang
2. Jayadev Acharya
3. Badih Ghazi
4. Ninghui Li
5. Ú. Erlingsson
6. Henry Corrigan-Gibbs
7. Marco Gaboardi
8. Vitaly Feldman
9. Tejas D. Kulkarni
10. Jinyuan Jia

Papers from Vasyl Pihur's perspective

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

1. Gradient-based learning applied to document recognition
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Learning Multiple Layers of Features from Tiny Images
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Attention is All you Need (Neural Information Processing Systems)
10. Calibrating Noise to Sensitivity in Private Data Analysis

Their papers

• RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response (2014) — 2,273 citations
• Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries (2015) — 318 citations
• Differentially-Private "Draw and Discard" Machine Learning (2018) — 39 citations