Zobrazeno 1 - 10
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pro vyhledávání: '"Gramfort, A."'
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
Autor:
Linhart, Julia, Cardoso, Gabriel Victorino, Gramfort, Alexandre, Corff, Sylvain Le, Rodrigues, Pedro L. C.
Determining which parameters of a non-linear model best describe a set of experimental data is a fundamental problem in science and it has gained much traction lately with the rise of complex large-scale simulators. The likelihood of such models is t
Externí odkaz:
http://arxiv.org/abs/2404.07593
Autor:
Westner, Britta U., McCloy, Daniel R., Larson, Eric, Gramfort, Alexandre, Katz, Daniel S., Smith, Arfon M., co-signees, invited
Most scientists need software to perform their research (Barker et al., 2020; Carver et al., 2022; Hettrick, 2014; Hettrick et al., 2014; Switters and Osimo, 2019), and neuroscientists are no exception. Whether we work with reaction times, electrophy
Externí odkaz:
http://arxiv.org/abs/2403.19394
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
Linear Independent Component Analysis (ICA) is a blind source separation technique that has been used in various domains to identify independent latent sources from observed signals. In order to obtain a higher signal-to-noise ratio, the presence of
Externí odkaz:
http://arxiv.org/abs/2312.00484
Autor:
Poldrack, Russell A., Markiewicz, Christopher J., Appelhoff, Stefan, Ashar, Yoni K., Auer, Tibor, Baillet, Sylvain, Bansal, Shashank, Beltrachini, Leandro, Benar, Christian G., Bertazzoli, Giacomo, Bhogawar, Suyash, Blair, Ross W., Bortoletto, Marta, Boudreau, Mathieu, Brooks, Teon L., Calhoun, Vince D., Castelli, Filippo Maria, Clement, Patricia, Cohen, Alexander L, Cohen-Adad, Julien, D'Ambrosio, Sasha, de Hollander, Gilles, de la iglesia-Vayá, María, de la Vega, Alejandro, Delorme, Arnaud, Devinsky, Orrin, Draschkow, Dejan, Duff, Eugene Paul, DuPre, Elizabeth, Earl, Eric, Esteban, Oscar, Feingold, Franklin W., Flandin, Guillaume, galassi, anthony, Gallitto, Giuseppe, Ganz, Melanie, Gau, Rémi, Gholam, James, Ghosh, Satrajit S., Giacomel, Alessio, Gillman, Ashley G, Gleeson, Padraig, Gramfort, Alexandre, Guay, Samuel, Guidali, Giacomo, Halchenko, Yaroslav O., Handwerker, Daniel A., Hardcastle, Nell, Herholz, Peer, Hermes, Dora, Honey, Christopher J., Innis, Robert B., Ioanas, Horea-Ioan, Jahn, Andrew, Karakuzu, Agah, Keator, David B., Kiar, Gregory, Kincses, Balint, Laird, Angela R., Lau, Jonathan C., Lazari, Alberto, Legarreta, Jon Haitz, Li, Adam, Li, Xiangrui, Love, Bradley C., Lu, Hanzhang, Maumet, Camille, Mazzamuto, Giacomo, Meisler, Steven L., Mikkelsen, Mark, Mutsaerts, Henk, Nichols, Thomas E., Nikolaidis, Aki, Nilsonne, Gustav, Niso, Guiomar, Norgaard, Martin, Okell, Thomas W, Oostenveld, Robert, Ort, Eduard, Park, Patrick J., Pawlik, Mateusz, Pernet, Cyril R., Pestilli, Franco, Petr, Jan, Phillips, Christophe, Poline, Jean-Baptiste, Pollonini, Luca, Raamana, Pradeep Reddy, Ritter, Petra, Rizzo, Gaia, Robbins, Kay A., Rockhill, Alexander P., Rogers, Christine, Rokem, Ariel, Rorden, Chris, Routier, Alexandre, Saborit-Torres, Jose Manuel, Salo, Taylor, Schirner, Michael, Smith, Robert E., Spisak, Tamas, Sprenger, Julia, Swann, Nicole C., Szinte, Martin, Takerkart, Sylvain, Thirion, Bertrand, Thomas, Adam G., Torabian, Sajjad, Varoquaux, Gael, Voytek, Bradley, Welzel, Julius, Wilson, Martin, Yarkoni, Tal, Gorgolewski, Krzysztof J.
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time.
Externí odkaz:
http://arxiv.org/abs/2309.05768
Autor:
Aristimunha, Bruno, de Camargo, Raphael Y., Pinaya, Walter H. Lopez, Chevallier, Sylvain, Gramfort, Alexandre, Rommel, Cedric
Electroencephalography (EEG) decoding is a challenging task due to the limited availability of labelled data. While transfer learning is a promising technique to address this challenge, it assumes that transferable data domains and task are known, wh
Externí odkaz:
http://arxiv.org/abs/2308.02408