Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Emmanuel de Bézenac"'
Publikováno v:
Machine Learning. 111:2349-2380
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::427c8023fe8b26d8d1f5aac93057a32f
https://doi.org/10.1007/s10994-022-06139-2
https://doi.org/10.1007/s10994-022-06139-2
Publikováno v:
Frontiers in Psychology, Vol 9 (2018)
While distinguishing between the actions and physical boundaries of self and other (non-self) is usually straightforward there are contexts in which such differentiation is challenging. For example, self–other ambiguity may occur when actions of ot
Externí odkaz:
https://doaj.org/article/36c93401f6f948aa9eaae42a4270bd28
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783030676605
ECML/PKDD (2)
ECML PKDD
ECML PKDD, Sep 2020, Ghent, Belgium
ECML/PKDD (2)
ECML PKDD
ECML PKDD, Sep 2020, Ghent, Belgium
Neural networks have been achieving high generalization performance on many tasks despite being highly over-parameterized. Since classical statistical learning theory struggles to explain this behavior, much effort has recently been focused on uncove
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f54d010cf2ab8073e1295680d8c4287
https://doi.org/10.1007/978-3-030-67661-2_7
https://doi.org/10.1007/978-3-030-67661-2_7
Publikováno v:
Machine Learning and Knowledge Discovery in Databases. Research Track ISBN: 9783030865191
ECML/PKDD (2)
ECML/PKDD (2)
Unsupervised Domain Translation (UDT) is the problem of finding a meaningful correspondence between two given domains, without explicit pairings between elements of the domains. Following the seminal CycleGAN model, variants and extensions have been
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c2c438825f5d63687daaa30568b959ca
https://doi.org/10.1007/978-3-030-86520-7_9
https://doi.org/10.1007/978-3-030-86520-7_9
Publikováno v:
ICASSP
We consider the problem of automatically learning the dynamics of physical processes evolving in space and time from incomplete observations. This is a central problem in many fields that remains complicated for large observation spaces and complex d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9f1239a8c68f91d1ab8f824950ecfc95
https://doi.org/10.1109/icassp40776.2020.9053035
https://doi.org/10.1109/icassp40776.2020.9053035
We consider the use of Deep Learning methods for modeling complex phenomena like those occurring in natural physical processes. With the large amount of data gathered on these phenomena the data intensive paradigm could begin to challenge more tradit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::68ee0d6464670ee57e0153928f2808a1
http://arxiv.org/abs/1711.07970
http://arxiv.org/abs/1711.07970
Publikováno v:
Journal of Statistical Mechanics: Theory & Experiment; Dec2019, Vol. 2019 Issue 12, p1-1, 1p
Autor:
Vincent Le Guen, Yuan Yin, Jérémie Dona, Ibrahim Ayed, Emmanuel de Bézenac, Nicolas Thome, Patrick Gallinari
Publikováno v:
HAL
Ninth International Conference on Learning Representations ICLR 2021
Ninth International Conference on Learning Representations ICLR 2021, 2021, Vienne (virtual), Austria
Ninth International Conference on Learning Representations ICLR 2021
Ninth International Conference on Learning Representations ICLR 2021, 2021, Vienne (virtual), Austria
Forecasting complex dynamical phenomena in settings where only partial knowledge of their dynamics is available is a prevalent problem across various scientific fields. While purely data-driven approaches are arguably insufficient in this context, st
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::57888967350460e062d1f8ceed825898
https://hal.archives-ouvertes.fr/hal-03137025
https://hal.archives-ouvertes.fr/hal-03137025