Laurent Condat

Computational Mechanics · 2012–2020

A researcher in the Science corpus: 8 papers; trusts 96 researchers and is trusted by 55 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 120 citations into it; #370 of 10,001 by trust here, #2,203 by citations
• Optimization & Training — 69 citations into it
• Computational Theory and Mathematics — 54 citations into it; #380 of 12,182 by trust here, #3,681 by citations
• Artificial Intelligence — 53 citations into it
• Mathematical Physics — 51 citations into it
• Numerical Analysis — 44 citations into it; #105 of 3,586 by trust here, #661 by citations
• Computer Vision and Pattern Recognition — 31 citations into it; #5,257 of 21,575 by trust here, #14,151 by citations
• Statistics and Probability — 23 citations into it

Whom Laurent Condat trusts overall

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

1. Patrick L. Combettes
2. Donald Ervin Knuth
3. Heinz H. Bauschke
4. Antonin Chambolle
5. Tom Goldstein
6. Amir Beck
7. Pierre-Louis Lions
8. Ming Yan
9. Jean-Christophe Pesquet
10. Stanley J. Osher

Who trusts Laurent Condat overall

1. Ming Yan
2. Antonin Chambolle
3. Damek Davis
4. Wotao Yin
5. Adil Salim
6. Tianyu Wu
7. G. Peyr'e
8. Thomas Pock
9. S. Chraibi
10. Peter Richtárik

Papers from Laurent Condat's perspective

Ordered by how strongly the researchers Laurent Condat 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. Regression Shrinkage and Selection via the Lasso
6. ImageNet: A large-scale hierarchical image database
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. Learning Multiple Layers of Features from Tiny Images
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Fast projection onto the simplex and the l1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pmb {l}_\mathbf {1}$$\end{ (2015) — 295 citations
• A Direct Algorithm for 1-D Total Variation Denoising (2013) — 368 citations
• A Primal–Dual Splitting Method for Convex Optimization Involving Lipschitzian, Proximable and Linear Composite Terms (2012) — 1,002 citations
• Dualize, Split, Randomize: Fast Nonsmooth Optimization Algorithms (2020) — 20 citations
• Proximal Splitting Algorithms: A Tour of Recent Advances, with New Twists. (2020) — 28 citations
• Distributed Proximal Splitting Algorithms with Rates and Acceleration (2020) — 24 citations
• From Local SGD to Local Fixed Point Methods for Federated Learning (2020) — 139 citations
• A Fast Projection onto the Simplex and the l 1 Ball (2015)