Zobrazeno 1 - 10
of 83
pro vyhledávání: '"Ullrich Köthe"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-16 (2023)
Abstract Mathematical models of cognition are often memoryless and ignore potential fluctuations of their parameters. However, human cognition is inherently dynamic. Thus, we propose to augment mechanistic cognitive models with a temporal dimension a
Externí odkaz:
https://doaj.org/article/b1e651ffdb8d4d4ebfedac4051935824
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 82, Iss 2, Pp 1-10 (2022)
Abstract The inference of physical parameters from measured distributions constitutes a core task in physics data analyses. Among recent deep learning methods, so-called conditional invertible neural networks provide an elegant approach owing to thei
Externí odkaz:
https://doaj.org/article/bfebe457274b4a3c954818fd2119e3bd
Towards learned emulation of interannual water isotopologue variations in General Circulation Models
Publikováno v:
Environmental Data Science, Vol 2 (2023)
Simulating abundances of stable water isotopologues, that is, molecules differing in their isotopic composition, within climate models allows for comparisons with proxy data and, thus, for testing hypotheses about past climate and validating climate
Externí odkaz:
https://doaj.org/article/be21907134e74ba4b174f526501d1de5
Autor:
Stefan T. Radev, Frederik Graw, Simiao Chen, Nico T. Mutters, Vanessa M. Eichel, Till Bärnighausen, Ullrich Köthe
Publikováno v:
PLoS Computational Biology, Vol 17, Iss 10 (2021)
Mathematical models in epidemiology are an indispensable tool to determine the dynamics and important characteristics of infectious diseases. Apart from their scientific merit, these models are often used to inform political decisions and interventio
Externí odkaz:
https://doaj.org/article/700dce7161f14b92b9338f3773914f5d
Autor:
Jens Kleesiek, Benedikt Kersjes, Kai Ueltzhöffer, Jacob M. Murray, Carsten Rother, Ullrich Köthe, Heinz-Peter Schlemmer
Publikováno v:
Cancers, Vol 13, Iss 13, p 3108 (2021)
Modern generative deep learning (DL) architectures allow for unsupervised learning of latent representations that can be exploited in several downstream tasks. Within the field of oncological medical imaging, we term these latent representations “d
Externí odkaz:
https://doaj.org/article/e01e0507c74e41bc8beb6e7cd2129766
The socio-economic impacts of rare extreme events, such as droughts, are one of the main ways in which climate affects humanity. A key challenge is to quantify the changing risk of once-in-a-decade or even once-in-a-century events under global warmin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3447dd48ffecdd19a844749a6758e809
https://doi.org/10.5194/egusphere-egu22-8656
https://doi.org/10.5194/egusphere-egu22-8656
Autor:
Anne C Bischops, Stefan T Radev, Ullrich Köthe, Simiao Chen, Pascal Geldsetzer, Malabika Sarker, Tin Tin Su, Fawzia Ahmed Mohamed, Noorali Darwish, Noor Ani Ahmad, Sidi Ahmed Ould Baba, Till Bärnighausen, Sandra Barteit
Publikováno v:
International journal of epidemiology.
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031185229
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cc332bd910c53e8cd49b7b74cf42d78c
https://doi.org/10.1007/978-3-031-18523-6_9
https://doi.org/10.1007/978-3-031-18523-6_9
Publikováno v:
The European physical journal / C 82(2), 171 (2022). doi:10.1140/epjc/s10052-022-10138-x
European Physical Journal
European Physical Journal
The inference of physical parameters from measured distributions constitutes a core task in physics data analyses. Among recent deep learning methods, so-called conditional invertible neural networks provide an elegant approach owing to their probabi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6184e0cc876df9c45a3026952bf82af5
http://arxiv.org/abs/2110.09493
http://arxiv.org/abs/2110.09493
Autor:
Frederik Graw, Simiao Chen, Nico T. Mutters, Stefan T. Radev, Till Bärnighausen, Vanessa M. Eichel, Ullrich Köthe
Publikováno v:
PLoS Computational Biology
PLoS Computational Biology, Vol 17, Iss 10, p e1009472 (2021)
PLoS Computational Biology, Vol 17, Iss 10 (2021)
PLoS Computational Biology, Vol 17, Iss 10, p e1009472 (2021)
PLoS Computational Biology, Vol 17, Iss 10 (2021)
Mathematical models in epidemiology are an indispensable tool to determine the dynamics and important characteristics of infectious diseases. Apart from their scientific merit, these models are often used to inform political decisions and interventio