Bargav Jayaraman

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 71 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 134 citations into it
• Privacy & Security — 74 citations into it; #666 of 709 by trust here, #615 by citations
• Safety, Robustness & Interpretability — 50 citations into it; #740 of 2,251 by trust here, #1,249 by citations
• Sociology and Political Science — 17 citations into it
• Optimization & Training — 16 citations into it
• Computational Theory and Mathematics — 15 citations into it
• Statistics and Probability — 14 citations into it
• Federated & Distributed Learning — 11 citations into it; #308 of 319 by trust here, #301 by citations

Whom Bargav Jayaraman trusts overall

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

1. Alex Krizhevsky
2. Reza Shokri
3. Cynthia Dwork
4. Kamalika Chaudhuri
5. Frank McSherry
6. Mark Bun
7. Diederik P. Kingma
8. Nhathai Phan
9. Kobbi Nissim
10. Vitaly Shmatikov

Who trusts Bargav Jayaraman overall

1. Natalia Ponomareva
2. Matthew Jagielski
3. Maria Rigaki
4. Nicholas Carlini
5. Ú. Erlingsson
6. Sanghyun Hong
7. César Sabater
8. Lun Wang
9. Hussein Hazimeh
10. Sebastian Garcia

Papers from Bargav Jayaraman's perspective

Ordered by how strongly the researchers Bargav Jayaraman 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. Gradient-based learning applied to document recognition
4. Learning Multiple Layers of Features from Tiny Images
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
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

• Evaluating Differentially Private Machine Learning in Practice (2019) — 613 citations
• When Relaxations Go Bad: "Differentially-Private" Machine Learning (2019) — 7 citations
• Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization (2018) — 174 citations