Adrià Gascón

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 13 papers; trusts 165 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 626 citations into it; #5,364 of 35,345 by trust here, #2,278 by citations
• Privacy & Security — 346 citations into it; #108 of 709 by trust here, #93 by citations
• Federated & Distributed Learning — 134 citations into it
• Computational Theory and Mathematics — 123 citations into it
• Optimization & Training — 115 citations into it
• Safety, Robustness & Interpretability — 101 citations into it
• Computer Vision and Pattern Recognition — 90 citations into it; #21,267 of 21,575 by trust here, #16,787 by citations
• Computer Networks and Communications — 67 citations into it

Whom Adrià Gascón trusts overall

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

1. Yehuda Lindell
2. Oded Goldreich
3. Cynthia Dwork
4. Samee Zahur
5. Moni Naor
6. David E. Evans
7. Benny Pinkas
8. Thomas Schneider
9. Daniel Demmler
10. Wenliang Du

Who trusts Adrià Gascón overall

1. Badih Ghazi
2. Peter Kairouz
3. Yang Liu
4. Ali Shahin Shamsabadi
5. Nitin Agrawal
6. Qiang Yang
7. Albert Cheu
8. Borja Balle
9. David E. Evans
10. Assi Barak

Papers from Adrià Gascón's perspective

Ordered by how strongly the researchers Adrià Gascón 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Learning Multiple Layers of Features from Tiny Images
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. ImageNet: A large-scale hierarchical image database
8. Long Short-Term Memory
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Advances and Open Problems in Federated Learning (2019) — 9,019 citations
• Blind Justice: Fairness with Encrypted Sensitive Attributes (2018) — 167 citations
• The Privacy Blanket of the Shuffle Model (2019) — 283 citations
• Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017) — 228 citations
• Differentially Private Summation with Multi-Message Shuffling (2019) — 48 citations
• QUOTIENT: Two-Party Secure Neural Network Training and Prediction (2019) — 245 citations
• Secure Single-Server Aggregation with (Poly)Logarithmic Overhead (2020) — 580 citations
• PrivEdge: From Local to Distributed Private Training and Prediction (2020) — 21 citations
• Private Summation in the Multi-Message Shuffle Model (2020) — 121 citations
• Improved Summation from Shuffling (2019) — 23 citations
• Private Nearest Neighbors Classification in Federated Databases (2018) — 16 citations
• TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service (2018) — 135 citations
• Secure Linear Regression on Vertically Partitioned Datasets (2016) — 108 citations