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pro vyhledávání: '"Banville A"'
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
Publikováno v:
Journal of Teaching in Physical Education; Oct2024, Vol. 43 Issue 4, p664-674, 11p
Autor:
Marttinen, Risto, Banville, Dominique, Holincheck, Nancy, Ferrer Lindsay, Vernise June, Stehle, Stephanie
Publikováno v:
Journal of Teaching in Physical Education; Oct2024, Vol. 43 Issue 4, p654-663, 10p
Food webs are complex ecological networks whose structure is both ecologically and statistically constrained, with many network properties being correlated with each other. Despite the recognition of these invariable relationships in food webs, the u
Externí odkaz:
http://arxiv.org/abs/2210.03190
Autor:
Tanya Strydom, Salomé Bouskila, Francis Banville, Ceres Barros, Dominique Caron, Maxwell J. Farrell, Marie‐Josée Fortin, Benjamin Mercier, Laura J. Pollock, Rogini Runghen, Giulio V. Dalla Riva, Timothée Poisot
Publikováno v:
Methods in Ecology and Evolution, Vol 14, Iss 12, Pp 2917-2930 (2023)
Abstract Metawebs (networks of potential interactions within a species pool) are a powerful abstraction to understand how large‐scale species interaction networks are structured. Because metawebs are typically expressed at large spatial and taxonom
Externí odkaz:
https://doaj.org/article/972b46184a5345c5908902156dd2145e
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
Publikováno v:
Journal of Teaching in Physical Education; Apr2024, Vol. 43 Issue 2, p352-361, 10p
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
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