167 citations · 342 across the 13 of their papers we have counts for
14 papers
Multi-Source Domain Adaptation meets Dataset Distillation through Dataset Dictionary Learning
Eduardo Fernandes Montesuma, Fred Ngolè Mboula, Antoine Souloumiac
In this paper, we consider the intersection of two problems in machine learning: Multi-Source Domain Adaptation (MSDA) and Dataset Distillation (DD). On the one hand, the first con…
Federated Dataset Dictionary Learning for Multi-Source Domain Adaptation
Fabiola Espinoza Castellon, Eduardo Fernandes Montesuma, Fred Ngolè Mboula +3
In this article, we propose an approach for federated domain adaptation, a setting where distributional shift exists among clients and some have unlabeled data. The proposed framew…
Benchmarking Domain Adaptation for Chemical Processes on the Tennessee Eastman Process
Eduardo Fernandes Montesuma, Michela Mulas, Fred Ngolè Mboula +2
In system monitoring, automatic fault diagnosis seeks to infer the systems' state based on sensor readings, e.g., through machine learning models. In this context, it is of key imp…
Multi-Source Domain Adaptation through Dataset Dictionary Learning in Wasserstein Space
Eduardo Fernandes Montesuma, Fred Ngolè Mboula, Antoine Souloumiac
This paper seeks to solve Multi-Source Domain Adaptation (MSDA), which aims to mitigate data distribution shifts when transferring knowledge from multiple labeled source domains to…
Recent Advances in Optimal Transport for Machine Learning
Eduardo Fernandes Montesuma, Fred Ngolè Mboula, Antoine Souloumiac
Recently, Optimal Transport has been proposed as a probabilistic framework in Machine Learning for comparing and manipulating probability distributions. This is rooted in its rich…
Galaxy Image Restoration with Shape Constraint
Fadi Nammour, Morgan A. Schmitz, Fred Maurice Ngolè Mboula +2
Images acquired with a telescope are blurred and corrupted by noise. The blurring is usually modeled by a convolution with the Point Spread Function and the noise by Additive Gauss…
Semi-supervised dictionary learning with graph regularization and active points
Khanh-Hung Tran, Fred-Maurice Ngole-Mboula, Jean-Luc Starck +1
Supervised Dictionary Learning has gained much interest in the recent decade and has shown significant performance improvements in image classification. However, in general, superv…
Euclid: Nonparametric point spread function field recovery through interpolation on a graph Laplacian
M. A. Schmitz, J. -L. Starck, F. Ngole Mboula +35
Context. Future weak lensing surveys, such as the Euclid mission, will attempt to measure the shapes of billions of galaxies in order to derive cosmological information. These surv…
Semi-supervised dual graph regularized dictionary learning
Khanh-Hung Tran, Fred-Maurice Ngole-Mboula, Jean-Luc Starck
In this paper, we propose a semi-supervised dictionary learning method that uses both the information in labelled and unlabelled data and jointly trains a linear classifier embedde…
Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning
Morgan A. Schmitz, Matthieu Heitz, Nicolas Bonneel +5
This paper introduces a new nonlinear dictionary learning method for histograms in the probability simplex. The method leverages optimal transport theory, in the sense that our aim…
Space variant deconvolution of galaxy survey images
Samuel Farrens, Jean-Luc Starck, Fred Maurice Ngolè Mboula
Removing the aberrations introduced by the Point Spread Function (PSF) is a fundamental aspect of astronomical image processing. The presence of noise in observed images makes deco…
GREAT3 results I: systematic errors in shear estimation and the impact of real galaxy morphology
Rachel Mandelbaum, Barnaby Rowe, Robert Armstrong +40
We present first results from the third GRavitational lEnsing Accuracy Testing (GREAT3) challenge, the third in a sequence of challenges for testing methods of inferring weak gravi…
Super-resolution method using sparse regularization for point-spread function recovery
Fred Maurice Ngolè Mboula, Jean-Luc Starck, Samuel Ronayette +2
In large-scale spatial surveys, such as the forthcoming ESA Euclid mission, images may be undersampled due to the optical sensors sizes. Therefore, one may consider using a super-r…
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
L. Jacques, C. De Vleeschouwer, Y. Boursier +64
The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between interna…