Aurélien Bellet

Artificial Intelligence · 2012–2021

A researcher in the Science corpus: 18 papers; trusts 459 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

Artificial Intelligence — 680 citations into it; #8,520 of 35,345 by trust here, #2,620 by citations
Privacy & Security — 276 citations into it; #484 of 709 by trust here, #478 by citations
Optimization & Training — 184 citations into it; #1,522 of 2,601 by trust here, #1,537 by citations
Federated & Distributed Learning — 166 citations into it; #109 of 319 by trust here, #31 by citations
Computer Networks and Communications — 149 citations into it; #4,079 of 9,886 by trust here, #3,228 by citations
Computer Vision and Pattern Recognition — 120 citations into it; #6,328 of 21,575 by trust here, #9,079 by citations
Safety, Robustness & Interpretability — 100 citations into it
Computational Mechanics — 75 citations into it; #3,657 of 10,001 by trust here, #6,287 by citations

Whom Aurélien Bellet trusts overall

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

Who trusts Aurélien Bellet overall

Papers from Aurélien Bellet's perspective

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

Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
ImageNet classification with deep convolutional neural networks
Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
Gradient-based learning applied to document recognition
Long Short-Term Memory
Attention is All you Need (Neural Information Processing Systems)
ImageNet: A large-scale hierarchical image database
Learning Multiple Layers of Features from Tiny Images
Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

Advances and Open Problems in Federated Learning (2019) — 9,019 citations
A Survey on Metric Learning for Feature Vectors and Structured Data (2013) — 714 citations
Similarity Learning for Provably Accurate Sparse Linear Classification (2012) — 57 citations
Robustness and generalization for metric learning (2012) — 104 citations
Decentralized Collaborative Learning of Personalized Models over Networks (2016) — 252 citations
Personalized and Private Peer-to-Peer Machine Learning (2017) — 172 citations
Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion? (2019) — 84 citations
Federated Multi-Task Learning under a Mixture of Distributions (2021) — 398 citations
Distributed Differentially Private Averaging with Improved Utility and Robustness to Malicious Parties (2020) — 26 citations
Privacy Amplification by Decentralization (2020) — 50 citations
Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs (2019) — 90 citations
Who started this rumor? Quantifying the natural differential privacy guarantees of gossip protocols (2019) — 9 citations
Hiding in the Crowd: A Massively Distributed Algorithm for Private Averaging with Malicious Adversaries (2018) — 8 citations
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions (2016) — 113 citations
Extending Gossip Algorithms to Distributed Estimation of U-statistics (2015) — 16 citations
Metric Learning (2015) — 196 citations
A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning (2014) — 48 citations
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets (2014) — 66 citations