Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Julius Berner"'
Autor:
Claudio M. Verdun, Tim Fuchs, Pavol Harar, Dennis Elbrächter, David S. Fischer, Julius Berner, Philipp Grohs, Fabian J. Theis, Felix Krahmer
Publikováno v:
Frontiers in Public Health, Vol 9 (2021)
Background: Due to the ongoing COVID-19 pandemic, demand for diagnostic testing has increased drastically, resulting in shortages of necessary materials to conduct the tests and overwhelming the capacity of testing laboratories. The supply scarcity a
Externí odkaz:
https://doaj.org/article/bec6e7ffd46147e8ae24995e9d58e959
Publikováno v:
Mitteilungen der Deutschen Mathematiker-Vereinigung. 29:191-197
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::27598abb4c65dd2d48e2343af1ea6db0
https://doi.org/10.1515/dmvm-2021-0074
https://doi.org/10.1515/dmvm-2021-0074
Publikováno v:
Mathematical Aspects of Deep Learning ISBN: 9781009025096
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e3718bc8d533560e7df88d4a417f8928
https://doi.org/10.1017/9781009025096.002
https://doi.org/10.1017/9781009025096.002
Autor:
Tim Fuchs, David S. Fischer, Claudio Mayrink Verdun, Dennis Elbrächter, Pavol Harar, Felix Krahmer, Julius Berner, Fabian J. Theis, Philipp Grohs
Publikováno v:
Front. Publ. Health 9:583377 (2021)
Frontiers in Public Health. 2021, vol. 9, issue 1, p. 1205-1218.
Frontiers in Public Health
Frontiers in Public Health. 2021, vol. 9, issue 1, p. 1205-1218.
Frontiers in Public Health
Summary Background Due to the ongoing COVID-19 pandemic, demand for diagnostic testing has increased drastically, resulting in shortages of necessary materials to conduct the tests and overwhelming the capacity of testing laboratories. The supply sca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d248ea6c750c869e29b7ff126ecbf6d4
https://push-zb.helmholtz-muenchen.de/frontdoor.php?source_opus=62950
https://push-zb.helmholtz-muenchen.de/frontdoor.php?source_opus=62950
Publikováno v:
2019 13th International conference on Sampling Theory and Applications (SampTA).
Although for neural networks with locally Lipschitz continuous activation functions the classical derivative exists almost everywhere, the standard chain rule is in general not applicable. We will consider a way of introducing a derivative for neural
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5cb2289791994b2c4d252badbb4ffb3d
https://doi.org/10.1109/sampta45681.2019.9031005
https://doi.org/10.1109/sampta45681.2019.9031005
The development of new classification and regression algorithms based on empirical risk minimization (ERM) over deep neural network hypothesis classes, coined deep learning, revolutionized the area of artificial intelligence, machine learning, and da
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d80e7968af13800583beeb487624cad8
Publikováno v:
University of Vienna-u:cris
Neural network training is usually accomplished by solving a non-convex optimization problem using stochastic gradient descent. Although one optimizes over the networks parameters, the main loss function generally only depends on the realization of t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::91d13549cd533e632f09aaf83a859b16
https://ucris.univie.ac.at/portal/en/publications/how-degenerate-is-the-parametrization-of-neural-networks-with-the-relu-activation-function(63c77dff-bb26-4c2c-a900-b835aaad663c).html
https://ucris.univie.ac.at/portal/en/publications/how-degenerate-is-the-parametrization-of-neural-networks-with-the-relu-activation-function(63c77dff-bb26-4c2c-a900-b835aaad663c).html