Zobrazeno 1 - 10
of 432
pro vyhledávání: '"Souloumiac, A."'
Autor:
Montesuma, Eduardo Fernandes, Castellon, Fabiola Espinoza, Mboula, Fred Ngolè, Mayoue, Aurélien, Souloumiac, Antoine, Gouy-Pailler, Cédric
Multi-Source Domain Adaptation (MSDA) is a challenging scenario where multiple related and heterogeneous source datasets must be adapted to an unlabeled target dataset. Conventional MSDA methods often overlook that data holders may have privacy conce
Externí odkaz:
http://arxiv.org/abs/2407.11647
In this paper, we tackle Multi-Source Domain Adaptation (MSDA), a task in transfer learning where one adapts multiple heterogeneous, labeled source probability measures towards a different, unlabeled target measure. We propose a novel framework for M
Externí odkaz:
http://arxiv.org/abs/2404.10261
In this paper we explore domain adaptation through optimal transport. We propose a novel approach, where we model the data distributions through Gaussian mixture models. This strategy allows us to solve continuous optimal transport through an equival
Externí odkaz:
http://arxiv.org/abs/2403.13847
Publikováno v:
Solid Earth, Vol 15, Pp 1445-1463 (2024)
This study employs numerical simulations based on the limit analysis (LA) method to calculate the stress distribution in a model that includes a basal detachment, featuring the lateral termination of a generic fault under compression. We conduct 2500
Externí odkaz:
https://doaj.org/article/0c53861b1d45437c8c90989cada81108
Autor:
Castellon, Fabiola Espinoza, Montesuma, Eduardo Fernandes, Mboula, Fred Ngolè, Mayoue, Aurélien, Souloumiac, Antoine, Gouy-Pailler, Cédric
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 framework, FedDaDiL, tackles the resulting challenge through dictionary lear
Externí odkaz:
http://arxiv.org/abs/2309.07670
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 considers adapting multiple heterogeneous labeled source domains to an un
Externí odkaz:
http://arxiv.org/abs/2309.07666
Autor:
Montesuma, Eduardo Fernandes, Mulas, Michela, Mboula, Fred Ngolè, Corona, Francesco, Souloumiac, Antoine
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 importance that, based on historical data, these systems are able to gene
Externí odkaz:
http://arxiv.org/abs/2308.11247
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 an unlabeled target domain. We propose a novel MSDA framework based o
Externí odkaz:
http://arxiv.org/abs/2307.14953
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 history and theory, and has offered new solutions to different problem
Externí odkaz:
http://arxiv.org/abs/2306.16156
A new information theoretic condition is presented for reconstructing a discrete random variable $X$ based on the knowledge of a set of discrete functions of $X$. The reconstruction condition is derived from Shannon's 1953 lattice theory with two ent
Externí odkaz:
http://arxiv.org/abs/2306.15540