Zobrazeno 1 - 10
of 387
pro vyhledávání: '"Drougard, A."'
Autor:
Angelotti, Giorgio, Chanel, Caroline P. C., Pinto, Adam H. M., Lounis, Christophe, Chauffaut, Corentin, Drougard, Nicolas
The integration of physiological computing into mixed-initiative human-robot interaction systems offers valuable advantages in autonomous task allocation by incorporating real-time features as human state observations into the decision-making system.
Externí odkaz:
http://arxiv.org/abs/2402.05703
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-10 (2023)
Abstract Brain-computer interfaces (BCIs) allow direct communication between one’s central nervous system and a computer without any muscle movement hence by-passing the peripheral nervous system. They can restore disabled people’s ability to int
Externí odkaz:
https://doaj.org/article/46d6140dadde436481fe44ffaf266ce7
Offline estimation of the dynamical model of a Markov Decision Process (MDP) is a non-trivial task that greatly depends on the data available in the learning phase. Sometimes the dynamics of the model is invariant with respect to some transformations
Externí odkaz:
http://arxiv.org/abs/2112.09943
Learning a Markov Decision Process (MDP) from a fixed batch of trajectories is a non-trivial task whose outcome's quality depends on both the amount and the diversity of the sampled regions of the state-action space. Yet, many MDPs are endowed with i
Externí odkaz:
http://arxiv.org/abs/2111.10297
In Offline Model Learning for Planning and in Offline Reinforcement Learning, the limited data set hinders the estimate of the Value function of the relative Markov Decision Process (MDP). Consequently, the performance of the obtained policy in the r
Externí odkaz:
http://arxiv.org/abs/2105.13431
The training of autonomous agents often requires expensive and unsafe trial-and-error interactions with the environment. Nowadays several data sets containing recorded experiences of intelligent agents performing various tasks, spanning from the cont
Externí odkaz:
http://arxiv.org/abs/2010.01931
Random Forests (RFs) are strong machine learning tools for classification and regression. However, they remain supervised algorithms, and no extension of RFs to the one-class setting has been proposed, except for techniques based on second-class samp
Externí odkaz:
http://arxiv.org/abs/1611.01971
Autor:
Raphaëlle N. Roy, Marcel F. Hinss, Ludovic Darmet, Simon Ladouce, Emilie S. Jahanpour, Bertille Somon, Xiaoqi Xu, Nicolas Drougard, Frédéric Dehais, Fabien Lotte
Publikováno v:
Frontiers in Neuroergonomics, Vol 3 (2022)
As is the case in several research domains, data sharing is still scarce in the field of Brain-Computer Interfaces (BCI), and particularly in that of passive BCIs—i.e., systems that enable implicit interaction or task adaptation based on a user's m
Externí odkaz:
https://doaj.org/article/57b9b64b92d84a58b336639e8c981995
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Autor:
Inès Barthélémy, Nadège Calmels, Robert B. Weiss, Laurent Tiret, Adeline Vulin, Nicolas Wein, Cécile Peccate, Carole Drougard, Christophe Beroud, Nathalie Deburgrave, Jean-Laurent Thibaud, Catherine Escriou, Isabel Punzón, Luis Garcia, Jean-Claude Kaplan, Kevin M. Flanigan, France Leturcq, Stéphane Blot
Publikováno v:
Skeletal Muscle, Vol 10, Iss 1, Pp 1-22 (2020)
Abstract Background Canine models of Duchenne muscular dystrophy (DMD) are a valuable tool to evaluate potential therapies because they faithfully reproduce the human disease. Several cases of dystrophinopathies have been described in canines, but th
Externí odkaz:
https://doaj.org/article/4db5e8aca9c9431692a8b17b3323ddbf