1.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- CentraleSupélecFR21 papers
- Institut national de recherche en sciences et technologies du numériqueFR13 papers
- Université Paris-SaclayFR11 papers
- OPIS: OPtimisation Imagerie et SantéFR8 papers
- École Centrale ParisFR6 papers
- Bouygues (France)FR4 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- Laboratoire d'Informatique Gaspard-MongeFR4 papers
- ESIEE ParisFR3 papers
- KU LeuvenBE3 papers
- Université de MontréalCA3 papers
- Centre Inria de SaclayFR2 papers
5 papers · 1 filter
Sparse Signal Reconstruction for Nonlinear Models via Piecewise Rational Optimization
Arthur Marmin, Marc Castella, Jean-Christophe Pesquet +1
We propose a method to reconstruct sparse signals degraded by a nonlinear distortion and acquired at a limited sampling rate. Our method formulates the reconstruction problem as a…
Optimizing persistent homology based functions
Mathieu Carrière, Frédéric Chazal, Marc Glisse +2
Solving optimization tasks based on functions and losses with a topological flavor is a very active, growing field of research in data science and Topological Data Analysis, with a…
NodeSig: Binary Node Embeddings via Random Walk Diffusion
Abdulkadir Çelikkanat, Fragkiskos D. Malliaros, Apostolos N. Papadopoulos
Graph Representation Learning (GRL) has become a key paradigm in network analysis, with a plethora of interdisciplinary applications. As the scale of networks increases, most of th…
Fixed Point Strategies in Data Science
Patrick L. Combettes, Jean-Christophe Pesquet
The goal of this paper is to promote the use of fixed point strategies in data science by showing that they provide a simplifying and unifying framework to model, analyze, and solv…
SPOQ -Over- Regularization for Sparse Signal Recovery applied to Mass Spectrometry
Afef Cherni, Emilie Chouzenoux, Laurent Duval +1
Underdetermined or ill-posed inverse problems require additional information for \ldd{d} sound solutions with tractable optimization algorithms. Sparsity yields consequent heuristi…