Jean-Christophe Pesquet
Computational Mechanics · 2007–2015
A researcher in the Science corpus: 6 papers; trusts 60 researchers and is trusted by 134 researchers here, as inferred from the citation graph.
Subfields
- Computational Mechanics — 67 citations into it; #155 of 10,001 by trust here, #403 by citations
- Computer Vision and Pattern Recognition — 50 citations into it; #5,008 of 21,575 by trust here, #11,721 by citations
- Mathematical Physics — 47 citations into it; #125 of 8,562 by trust here, #251 by citations
- Computational Theory and Mathematics — 45 citations into it; #757 of 12,182 by trust here, #2,351 by citations
- Optimization & Training — 27 citations into it; #338 of 2,601 by trust here, #620 by citations
- Numerical Analysis — 26 citations into it; #322 of 3,586 by trust here, #457 by citations
- Mathematical Optimization — 20 citations into it
- Numerical Methods & Scientific Computing — 15 citations into it
Whom Jean-Christophe Pesquet trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Patrick L. Combettes
- Heinz H. Bauschke
- Ingrid Daubechies
- R. Tyrrell Rockafellar
- Yurii E. Nesterov
- Constantin Zalinescu
- Valérie R. Wajs
- Antonin Chambolle
- Gabriele Steidl
- Francis R. Bach
Who trusts Jean-Christophe Pesquet overall
- Laurent Condat
- Peter J. Green
- Patrick L. Combettes
- Marcelo Pereyra
- Ming Yan
- Francis R. Bach
- Vassilis Kalofolias
- Brendan O'Donoghue
- Volkan Cevher
- K. Latuszy'nski
Papers from Jean-Christophe Pesquet's perspective
Ordered by how strongly the researchers Jean-Christophe Pesquet 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
- Gradient-based learning applied to document recognition
- Regression Shrinkage and Selection via the Lasso
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- 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)
- Long Short-Term Memory
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Playing with Duality: An overview of recent primal?dual approaches for solving large-scale optimization problems (2014) — 416 citations
- A Douglas–Rachford Splitting Approach to Nonsmooth Convex Variational Signal Recovery (2007) — 534 citations
- Proximal Splitting Methods in Signal Processing (2009) — 2,764 citations
- Primal-Dual Splitting Algorithm for Solving Inclusions with Mixtures of Composite, Lipschitzian, and Parallel-Sum Type Monotone Operators (2011) — 430 citations
- A proximal decomposition method for solving convex variational inverse problems (2008) — 300 citations
- Stochastic Approximations and Perturbations in Forward-Backward Splitting for Monotone Operators (2015) — 62 citations