The Necessity of Ambiguity in Self–Other Processing: A Psychosocial Perspective With Implications for Mental Health

Autor: Christophe Emmanuel de Bézenac, Rachel Ann Swindells, Rhiannon Corcoran

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

A Principle of Least Action for the Training of Neural Networks

Autor: Ibrahhim Ayed, Patrick Gallinari, Emmanuel de Bézenac, Skander Karkar

Publikováno v: Machine Learning and Knowledge Discovery in Databases ISBN: 9783030676605
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

Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge

Autor: Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari

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

Augmenting physical models with deep networks for complex dynamics forecasting

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

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