Zobrazeno 1 - 10
of 5 080
pro vyhledávání: '"Rivail"'
Autor:
Ruiz-López, Manuel F.1 Manuel.Ruiz@lctn.uhp-nancy.fr
Publikováno v:
Theoretical Chemistry Accounts: Theory, Computation, & Modeling. Sep2004, Vol. 112 Issue 4, p179-183. 5p.
Autor:
Chakravarty, Arunava, Emre, Taha, Lachinov, Dmitrii, Rivail, Antoine, Scholl, Hendrik, Fritsche, Lars, Sivaprasad, Sobha, Rueckert, Daniel, Lotery, Andrew, Schmidt-Erfurth, Ursula, Bogunović, Hrvoje
Predicting future disease progression risk from medical images is challenging due to patient heterogeneity, and subtle or unknown imaging biomarkers. Moreover, deep learning (DL) methods for survival analysis are susceptible to image domain shifts ac
Externí odkaz:
http://arxiv.org/abs/2409.20195
Autor:
Rivail Carvalho Rolim
Publikováno v:
Antíteses, Vol 8, Iss 15esp, Pp 272-293 (2015)
Inúmeras pesquisas analisaram variados aspectos acerca do exercício do poder durante o regime militar, destacando inclusive a repressão e a violência levada a cabo pelas forças repressivas contra os grupos políticos que se opuseram a esse regim
Externí odkaz:
https://doaj.org/article/3783d6e9d65c403e8c5cbd07bf078e12
Autor:
Emre, Taha, Chakravarty, Arunava, Lachinov, Dmitrii, Rivail, Antoine, Schmidt-Erfurth, Ursula, Bogunović, Hrvoje
Contrastive pretraining provides robust representations by ensuring their invariance to different image transformations while simultaneously preventing representational collapse. Equivariant contrastive learning, on the other hand, provides represent
Externí odkaz:
http://arxiv.org/abs/2405.09404
Publikováno v:
Biblioteca Digital de Teses e Dissertações do UNIOESTE
Universidade Estadual do Oeste do Paraná (UNIOESTE)
instacron:UNIOESTE
Universidade Estadual do Oeste do Paraná (UNIOESTE)
instacron:UNIOESTE
Submitted by Neusa Fagundes (neusa.fagundes@unioeste.br) on 2022-12-08T17:19:15Z No. of bitstreams: 2 Analides_Costa2022.pdf: 1632874 bytes, checksum: 0c14127d53bd9eedbcd4f95978ee2e93 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3056::f73cf089c88e4501a2c913a192a0498d
https://tede.unioeste.br/handle/tede/6342
https://tede.unioeste.br/handle/tede/6342
Autor:
José Márcio de Almeida
Publikováno v:
Revista Ibero-Americana de Humanidades, Ciências e Educação. 9:583-599
O interesse pelo tema abordado nesta pesquisa se deve ao fato de que a relação de J.H. Pestalozzi com o seu discípulo H.L.D. Rivail, futuro Allan Kardec, não está documentada, não obstante, os treze volumes de cartas do primeiro a amigos, famil
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bb593791fc56bb12dfa3d9b39bedf971
https://doi.org/10.51891/rease.v9i1.8279
https://doi.org/10.51891/rease.v9i1.8279
Autor:
Emre, Taha, Chakravarty, Arunava, Rivail, Antoine, Lachinov, Dmitrii, Leingang, Oliver, Riedl, Sophie, Mai, Julia, Scholl, Hendrik P. N., Sivaprasad, Sobha, Rueckert, Daniel, Lotery, Andrew, Schmidt-Erfurth, Ursula, Bogunović, Hrvoje
Self-supervised learning (SSL) has emerged as a powerful technique for improving the efficiency and effectiveness of deep learning models. Contrastive methods are a prominent family of SSL that extract similar representations of two augmented views o
Externí odkaz:
http://arxiv.org/abs/2312.16980
Autor:
Przemysław Paweł Grzybowski
Publikováno v:
Biuletyn Historii Wychowania. :5-12
Autor przedstawia sylwetkę Denizarda Rivaila - twórcy filozofii spirytystycznej, z której współcześnie wywodzi się podstawy popularnej zwłaszcza w Brazylii pedagogiki spirytystycznej. Poglądy D.Rivaila przyczyniły się do powstania teorii s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::150b0a95d56e9a5d5bdec386923fea0a
https://doi.org/10.14746/bhw.1999.9.10.1
https://doi.org/10.14746/bhw.1999.9.10.1
Autor:
Emre, Taha, Oghbaie, Marzieh, Chakravarty, Arunava, Rivail, Antoine, Riedl, Sophie, Mai, Julia, Scholl, Hendrik P. N., Sivaprasad, Sobha, Rueckert, Daniel, Lotery, Andrew, Schmidt-Erfurth, Ursula, Bogunović, Hrvoje
In the field of medical imaging, 3D deep learning models play a crucial role in building powerful predictive models of disease progression. However, the size of these models presents significant challenges, both in terms of computational resources an
Externí odkaz:
http://arxiv.org/abs/2307.13865