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pro vyhledávání: '"A. Collas"'
Machine learning applications on signals such as computer vision or biomedical data often face significant challenges due to the variability that exists across hardware devices or session recordings. This variability poses a Domain Adaptation (DA) pr
Externí odkaz:
http://arxiv.org/abs/2407.14303
Autor:
Lalou, Yanis, Gnassounou, Théo, Collas, Antoine, de Mathelin, Antoine, Kachaiev, Oleksii, Odonnat, Ambroise, Gramfort, Alexandre, Moreau, Thomas, Flamary, Rémi
Unsupervised Domain Adaptation (DA) consists of adapting a model trained on a labeled source domain to perform well on an unlabeled target domain with some data distribution shift. While many methods have been proposed in the literature, fair and rea
Externí odkaz:
http://arxiv.org/abs/2407.11676
Autor:
Mellot, Apolline, Collas, Antoine, Chevallier, Sylvain, Gramfort, Alexandre, Engemann, Denis A.
Electroencephalography (EEG) data is often collected from diverse contexts involving different populations and EEG devices. This variability can induce distribution shifts in the data $X$ and in the biomedical variables of interest $y$, thus limiting
Externí odkaz:
http://arxiv.org/abs/2407.03878
Combining electroencephalogram (EEG) datasets for supervised machine learning (ML) is challenging due to session, subject, and device variability. ML algorithms typically require identical features at train and test time, complicating analysis due to
Externí odkaz:
http://arxiv.org/abs/2403.15415
This paper introduces a novel domain adaptation technique for time series data, called Mixing model Stiefel Adaptation (MSA), specifically addressing the challenge of limited labeled signals in the target dataset. Leveraging a domain-dependent mixing
Externí odkaz:
http://arxiv.org/abs/2402.03345
When dealing with a parametric statistical model, a Riemannian manifold can naturally appear by endowing the parameter space with the Fisher information metric. The geometry induced on the parameters by this metric is then referred to as the Fisher-R
Externí odkaz:
http://arxiv.org/abs/2310.01032
Dimension reduction (DR) methods provide systematic approaches for analyzing high-dimensional data. A key requirement for DR is to incorporate global dependencies among original and embedded samples while preserving clusters in the embedding space. T
Externí odkaz:
http://arxiv.org/abs/2303.05119
Autor:
Evdokiia Potolitsyna, Sarah Hazell Pickering, Aurélie Bellanger, Thomas Germier, Philippe Collas, Nolwenn Briand
Publikováno v:
Communications Biology, Vol 7, Iss 1, Pp 1-11 (2024)
Abstract Differentiation of adipose progenitor cells into mature adipocytes entails a dramatic reorganization of the cellular architecture to accommodate lipid storage into cytoplasmic lipid droplets. Lipid droplets occupy most of the adipocyte volum
Externí odkaz:
https://doaj.org/article/d0587ce7dc8e4457ac15a5dd7b671745
We introduce the information geometry module of the Python package Geomstats. The module first implements Fisher-Rao Riemannian manifolds of widely used parametric families of probability distributions, such as normal, gamma, beta, Dirichlet distribu
Externí odkaz:
http://arxiv.org/abs/2211.11643
This paper studies the statistical model of the non-centered mixture of scaled Gaussian distributions (NC-MSG). Using the Fisher-Rao information geometry associated to this distribution, we derive a Riemannian gradient descent algorithm. This algorit
Externí odkaz:
http://arxiv.org/abs/2209.03315