Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Mohamed Tamaazousti"'
Publikováno v:
Journal of Physics Communications, Vol 7, Iss 5, p 055005 (2023)
In this paper, we investigate the large-time behavior for a slightly modified version of the standard p = 2 soft spins dynamics model, including a quartic or higher potential. The equilibrium states of such a model correspond to an effective field th
Externí odkaz:
https://doaj.org/article/e938d15fa9854c6098eb5b03920d803d
Publikováno v:
Symmetry, Vol 14, Iss 3, p 486 (2022)
The large scale behavior of systems having a large number of interacting degrees of freedom is suitably described using the renormalization group from non-Gaussian distributions. Renormalization group techniques used in physics are then expected to p
Externí odkaz:
https://doaj.org/article/9a639ef5bfd94361a9ff0ddb43c4aea2
Publikováno v:
Entropy, Vol 23, Iss 9, p 1132 (2021)
Renormalization group techniques are widely used in modern physics to describe the relevant low energy aspects of systems involving a large number of degrees of freedom. Those techniques are thus expected to be a powerful tool to address open issues
Externí odkaz:
https://doaj.org/article/40aacb8aab4741f4b9b9efcd914ff10a
Publikováno v:
IET Computer Vision, Vol 11, Iss 2, Pp 122-134 (2017)
Dense motion field estimation is a key computer vision problem. Many solutions have been proposed to compute small or large displacements, narrow or wide baseline stereo disparity, or non‐rigid surface registration, but a unified methodology is sti
Externí odkaz:
https://doaj.org/article/4eca564e5c684b63a66320b77d867cb2
Publikováno v:
Entropy, Vol 23, Iss 7, p 795 (2021)
The tensorial principal component analysis is a generalization of ordinary principal component analysis focusing on data which are suitably described by tensors rather than matrices. This paper aims at giving the nonperturbative renormalization group
Externí odkaz:
https://doaj.org/article/504f8a2f504942418d31a4a7d0e6c551
Publikováno v:
Revue Française de Photogrammétrie et de Télédétection, Iss 215 (2017)
Nous proposons d'explorer le potentiel de l'imagerie multispectrale appliquée à la complétion d'image, une thématique classique de vision par ordinateur qui consiste à automatiquement remplacer une zone de l'image de manière plausible. Pour ce
Externí odkaz:
https://doaj.org/article/7012000f28c74b788c6835e21e2a8d10
Publikováno v:
SciPost Physics Core, Vol 7, Iss 4, p 077 (2024)
This review paper utilizes renormalization group techniques for signal detection in nearly continuous positive spectra. We emphasize the universal aspects of the analogue field-theory approach. The primary objective is to present an extended self-con
Externí odkaz:
https://doaj.org/article/4be82f4c65fc41a6ac4dac64e68e0371
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 36:8204-8211
In this paper, based on an asymptotic analysis of the Softmax layer, we show that when training neural networks for classification tasks, the weight vectors corre sponding to each class of the Softmax layer tend to converge to the class-wise means co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::67379be5759dfa72ac94be101a43ebfd
https://doi.org/10.1609/aaai.v36i8.20794
https://doi.org/10.1609/aaai.v36i8.20794
Publikováno v:
J.Phys.Comm.
J.Phys.Comm., 2023, 7 (5), pp.055005. ⟨10.1088/2399-6528/acd09d⟩
J.Phys.Comm., 2023, 7 (5), pp.055005. ⟨10.1088/2399-6528/acd09d⟩
In this paper, we investigate the large-time behavior for a slightly modified version of the standard p=2 soft spins dynamics model, including a quartic or higher potential. The equilibrium states of such a model correspond to an effective field theo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ca46ecc3c72c2393960e6fe73ab66714
https://hal.science/hal-03921074
https://hal.science/hal-03921074
Autor:
Gaétan Landreau, Mohamed Tamaazousti
Publikováno v:
Advances in Visual Computing ISBN: 9783031207129
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2f9f425cd6b5671d8c13e61a9f408fb5
https://doi.org/10.1007/978-3-031-20713-6_31
https://doi.org/10.1007/978-3-031-20713-6_31