Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Oskar Taubert"'
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
Autor:
Oskar Taubert, Marie Weiel, Daniel Coquelin, Anis Farshian, Charlotte Debus, Alexander Schug, Achim Streit, Markus Götz
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031320408
We present Propulate, an evolutionary optimization algorithm and software package for global optimization and in particular hyperparameter search. For efficient use of HPC resources, Propulate omits the synchronization after each generation as done i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8a7f0e0ec11cedea07ff74f88108ce2a
https://doi.org/10.1007/978-3-031-32041-5_6
https://doi.org/10.1007/978-3-031-32041-5_6
Autor:
null James Kahn, null Ilias Tsaklidis, null Oskar Taubert, null Lea Reuter, null Giulio Dujany, null Tobias Böckh, null Arthur Thaller, null Pablo Goldenzweig, null Florian Bernlochner, null Achim Streit, null Markus Götz
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::78abf66b4b7207391b604f4959b09461
https://doi.org/10.1088/2632-2153/ac8de0/v2/response1
https://doi.org/10.1088/2632-2153/ac8de0/v2/response1
Autor:
James Kahn, Ilias Tsaklidis, Oskar Taubert, Lea Reuter, Giulio Dujany, Tobias Boeckh, Arthur Thaller, Pablo Goldenzweig, Florian Bernlochner, Achim Streit, Markus Götz
Publikováno v:
Mach.Learn.Sci.Tech.
Mach.Learn.Sci.Tech., 2022, 3 (3), pp.035012. ⟨10.1088/2632-2153/ac8de0⟩
Machine Learning: Science and Technology, 3 (3), Art.Nr. 035012
Mach.Learn.Sci.Tech., 2022, 3 (3), pp.035012. ⟨10.1088/2632-2153/ac8de0⟩
Machine Learning: Science and Technology, 3 (3), Art.Nr. 035012
In this work, we present a neural approach to reconstructing rooted tree graphs describing hierarchical interactions, using a novel representation we term the Lowest Common Ancestor Generations (LCAG) matrix. This compact formulation is equivalent to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e394440409a52269222c790781ca38c7
Publikováno v:
IEEE 426-431 (2020). doi:10.1109/ICMLA51294.2020.00073
2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), Miami, FL, 2020-12-14-2020-12-17
ICMLA
2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), Miami, FL, 2020-12-14-2020-12-17
ICMLA
When training a classifier the choice of loss function heavily influences the characteristics of the resulting model. The most commonly used loss function for classification is cross entropy. In image segmentation problems where each pixel is assigne
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8bff8b3f90e807474621010386daba48
https://publikationen.bibliothek.kit.edu/1000127713
https://publikationen.bibliothek.kit.edu/1000127713
Publikováno v:
Bioinformatics 35(24), 5337–5338 (2019). doi:10.1093/bioinformatics/btz578
Summary The distance geometry problem is often encountered in molecular biology and the life sciences at large, as a host of experimental methods produce ambiguous and noisy distance data. In this note, we present diSTruct; an adaptation of the gener
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::394d14ebdeb8e467b4b92213f74cb0e7
https://hdl.handle.net/2128/23915
https://hdl.handle.net/2128/23915