Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Fabrice von der Lehr"'
Autor:
Oskar Taubert, Fabrice von der Lehr, Alina Bazarova, Christian Faber, Philipp Knechtges, Marie Weiel, Charlotte Debus, Daniel Coquelin, Achim Basermann, Achim Streit, Stefan Kesselheim, Markus Götz, Alexander Schug
Publikováno v:
Communications Biology, Vol 6, Iss 1, Pp 1-8 (2023)
Abstract On the path to full understanding of the structure-function relationship or even design of RNA, structure prediction would offer an intriguing complement to experimental efforts. Any deep learning on RNA structure, however, is hampered by th
Externí odkaz:
https://doaj.org/article/d536d84aae0f4245a6e3998c83d5238a
Accelerating neural network training with distributed asynchronous and selective optimization (DASO)
Autor:
Daniel Coquelin, Charlotte Debus, Markus Götz, Fabrice von der Lehr, James Kahn, Martin Siggel, Achim Streit
Publikováno v:
Journal of Big Data, Vol 9, Iss 1, Pp 1-18 (2022)
Abstract With increasing data and model complexities, the time required to train neural networks has become prohibitively large. To address the exponential rise in training time, users are turning to data parallel neural networks (DPNN) and large-sca
Externí odkaz:
https://doaj.org/article/aa27344a1d45457eb832b4a93df6812a
Accelerating Neural Network Training with Distributed Asynchronous and Selective Optimization (DASO)
Autor:
Charlotte Debus, James Kahn, Achim Streit, Fabrice von der Lehr, Markus Götz, Daniel Coquelin, Martin Siggel
Publikováno v:
Journal of Big Data, Vol 9, Iss 1, Pp 1-18 (2022)
Journal of Big Data, 9 (1), 14
Journal of Big Data, 9 (1), 14
With increasing data and model complexities, the time required to train neural networks has become prohibitively large. To address the exponential rise in training time, users are turning to data parallel neural networks (DPNN) and large-scale distri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ca0d2a644e65022219b7949182b7e3f5
https://publikationen.bibliothek.kit.edu/1000137220
https://publikationen.bibliothek.kit.edu/1000137220
