Zobrazeno 1 - 10
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pro vyhledávání: '"Banville, Hubert"'
Autor:
Thual, Alexis, Benchetrit, Yohann, Geilert, Felix, Rapin, Jérémy, Makarov, Iurii, Banville, Hubert, King, Jean-Rémi
Deep learning is leading to major advances in the realm of brain decoding from functional Magnetic Resonance Imaging (fMRI). However, the large inter-subject variability in brain characteristics has limited most studies to train models on one subject
Externí odkaz:
http://arxiv.org/abs/2312.06467
In the past five years, the use of generative and foundational AI systems has greatly improved the decoding of brain activity. Visual perception, in particular, can now be decoded from functional Magnetic Resonance Imaging (fMRI) with remarkable fide
Externí odkaz:
http://arxiv.org/abs/2310.19812
Autor:
Banville, Hubert, Wood, Sean U. N., Aimone, Chris, Engemann, Denis-Alexander, Gramfort, Alexandre
Building machine learning models using EEG recorded outside of the laboratory setting requires methods robust to noisy data and randomly missing channels. This need is particularly great when working with sparse EEG montages (1-6 channels), often enc
Externí odkaz:
http://arxiv.org/abs/2105.12916
Autor:
Banville, Hubert, Chehab, Omar, Hyvärinen, Aapo, Engemann, Denis-Alexander, Gramfort, Alexandre
Objective. Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG), where labeling can be costly in
Externí odkaz:
http://arxiv.org/abs/2007.16104
Autor:
Banville, Hubert, Albuquerque, Isabela, Hyvärinen, Aapo, Moffat, Graeme, Engemann, Denis-Alexander, Gramfort, Alexandre
The supervised learning paradigm is limited by the cost - and sometimes the impracticality - of data collection and labeling in multiple domains. Self-supervised learning, a paradigm which exploits the structure of unlabeled data to create learning p
Externí odkaz:
http://arxiv.org/abs/1911.05419
Autor:
Roy, Yannick, Banville, Hubert, Albuquerque, Isabela, Gramfort, Alexandre, Falk, Tiago H., Faubert, Jocelyn
Electroencephalography (EEG) is a complex signal and can require several years of training to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good featur
Externí odkaz:
http://arxiv.org/abs/1901.05498
Autor:
Engemann, Denis A., Mellot, Apolline, Höchenberger, Richard, Banville, Hubert, Sabbagh, David, Gemein, Lukas, Ball, Tonio, Gramfort, Alexandre
Publikováno v:
In NeuroImage 15 November 2022 262
Autor:
Banville, Hubert, Wood, Sean U.N., Aimone, Chris, Engemann, Denis-Alexander, Gramfort, Alexandre
Publikováno v:
In NeuroImage 1 May 2022 251
Autor:
Banville, Hubert
Publikováno v:
Artificial Intelligence [cs.AI]. Université Paris-Saclay, 2022. English. ⟨NNT : 2022UPASG005⟩
Our understanding of the brain has improved considerably in the last decades, thanks to groundbreaking advances in the field of neuroimaging. Now, with the invention and wider availability of personal wearable neuroimaging devices, such as low-cost m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::b3d0243469731fa3bb2b4a95ab79cd76
https://theses.hal.science/tel-03602771/document
https://theses.hal.science/tel-03602771/document
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