activity
20112026
most citedSupervised machine learning on Galactic filaments Revealing the filamentary structure of the Galactic interstellar medium

21 citations · 45 across the 7 of their papers we have counts for

collaborators

7 papers

astro-ph.CO2026★ 1 cited

Owl-z: a Bayesian tool to select z \geq 7 quasars

Meriam Ezziati, Roser Pello, Jean-Gabriel Cuby +9

This paper presents Owl-z, a Bayesian code aiming at identifying z \geq 7 quasars in wide field optical and near-infrared surveys. By construction,the code can also be used to sele…

astro-ph.CO2023★ 11 cited

CONCERTO: Extracting the power spectrum of the [C II ] emission line

M. Van Cuyck, N. Ponthieu, G. Lagache +21

CONCERTO is the first experiment to perform a [CII] line intensity mapping survey to target . Measuring the [CII] power spectrum allows us to study the role of dusty star-fo…

astro-ph.GA2022★ 21 cited

Supervised machine learning on Galactic filaments Revealing the filamentary structure of the Galactic interstellar medium

A. Zavagno, F. -X. Dupé, S. Bensaid +11

Context. Filaments are ubiquitous in the Galaxy, and they host star formation. Detecting them in a reliable way is therefore key towards our understanding of the star formation pro…

cs.LG2022★ 1 cited

Kernelized multi-graph matching

François-Xavier Dupé, Rohit Yadav, Guillaume Auzias +1

Multigraph matching is a recent variant of the graph matching problem. In this framework, the optimization procedure considers several graphs and enforces the consistency of the ma…

cs.CV2020★ 8 cited

Mapping individual differences in cortical architecture using multi-view representation learning

Akrem Sellami, François-Xavier Dupé, Bastien Cagna +4

In neuroscience, understanding inter-individual differences has recently emerged as a major challenge, for which functional magnetic resonance imaging (fMRI) has proven invaluable.…

stat.AP2011★ 3 cited

Deconvolution under Poisson noise using exact data fidelity and synthesis or analysis sparsity priors

François-Xavier Dupé, Jalal Fadili, Jean-Luc Starck

In this paper, we propose a Bayesian MAP estimator for solving the deconvolution problems when the observations are corrupted by Poisson noise. Towards this goal, a proper data fid…