Zobrazeno 1 - 10
of 297
pro vyhledávání: '"Moreau, Thomas"'
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
Physiological signal analysis often involves identifying events crucial to understanding biological dynamics. Traditional methods rely on handcrafted procedures or supervised learning, presenting challenges such as expert dependence, lack of robustne
Externí odkaz:
http://arxiv.org/abs/2406.16938
Many modern spatio-temporal data sets, in sociology, epidemiology or seismology, for example, exhibit self-exciting characteristics, triggering and clustering behaviors both at the same time, that a suitable Hawkes space-time process can accurately c
Externí odkaz:
http://arxiv.org/abs/2406.06849
Autor:
Chevallier, Sylvain, Carrara, Igor, Aristimunha, Bruno, Guetschel, Pierre, Sedlar, Sara, Lopes, Bruna, Velut, Sebastien, Khazem, Salim, Moreau, Thomas
Objective. This study conduct an extensive Brain-computer interfaces (BCI) reproducibility analysis on open electroencephalography datasets, aiming to assess existing solutions and establish open and reproducible benchmarks for effective comparison w
Externí odkaz:
http://arxiv.org/abs/2404.15319
The interaction of a gravitational wave (GW) with an elastic body is usually described in terms of a GW "force" driving the oscillations of the body's normal modes. However, this description is only possible for GW frequencies for which the response
Externí odkaz:
http://arxiv.org/abs/2403.16550
Publikováno v:
9th Graz Brain-Computer Interface Conference (2024) 11-16
Motivated by the challenge of seamless cross-dataset transfer in EEG signal processing, this article presents an exploratory study on the use of Joint Embedding Predictive Architectures (JEPAs). In recent years, self-supervised learning has emerged a
Externí odkaz:
http://arxiv.org/abs/2403.11772
Plug-and-play algorithms constitute a popular framework for solving inverse imaging problems that rely on the implicit definition of an image prior via a denoiser. These algorithms can leverage powerful pre-trained denoisers to solve a wide range of
Externí odkaz:
http://arxiv.org/abs/2312.01831
Given observed data and a probabilistic generative model, Bayesian inference searches for the distribution of the model's parameters that could have yielded the data. Inference is challenging for large population studies where millions of measurement
Externí odkaz:
http://arxiv.org/abs/2308.16022
Implicit deep learning has recently gained popularity with applications ranging from meta-learning to Deep Equilibrium Networks (DEQs). In its general formulation, it relies on expressing some components of deep learning pipelines implicitly, typical
Externí odkaz:
http://arxiv.org/abs/2305.15042
Autor:
Bonet, Clément, Malézieux, Benoît, Rakotomamonjy, Alain, Drumetz, Lucas, Moreau, Thomas, Kowalski, Matthieu, Courty, Nicolas
When dealing with electro or magnetoencephalography records, many supervised prediction tasks are solved by working with covariance matrices to summarize the signals. Learning with these matrices requires using Riemanian geometry to account for their
Externí odkaz:
http://arxiv.org/abs/2303.05798