40 citations · 57 across the 10 of their papers we have counts for
13 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…
Deep learning for ECoG brain-computer interface: end-to-end vs. hand-crafted features
Maciej Śliwowski, Matthieu Martin, Antoine Souloumiac +2
In brain signal processing, deep learning (DL) models have become commonly used. However, the performance gain from using end-to-end DL models compared to conventional ML approache…