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pro vyhledávání: '"Heilenkötter, Nick"'
Autor:
Arndt, Clemens, Dittmer, Sören, Heilenkötter, Nick, Iske, Meira, Kluth, Tobias, Nickel, Judith
Learning-based methods for inverse problems, adapting to the data's inherent structure, have become ubiquitous in the last decade. Besides empirical investigations of their often remarkable performance, an increasing number of works addresses the iss
Externí odkaz:
http://arxiv.org/abs/2307.10431
Autor:
Beckmann, Matthias, Heilenkötter, Nick
In recent years, deep learning techniques have shown great success in various tasks related to inverse problems, where a target quantity of interest can only be observed through indirect measurements by a forward operator. Common approaches apply dee
Externí odkaz:
http://arxiv.org/abs/2306.16506
Autor:
Arndt, Clemens, Denker, Alexander, Dittmer, Sören, Heilenkötter, Nick, Iske, Meira, Kluth, Tobias, Maass, Peter, Nickel, Judith
Publikováno v:
Inverse Problems 39 125018 (2023)
Learned inverse problem solvers exhibit remarkable performance in applications like image reconstruction tasks. These data-driven reconstruction methods often follow a two-step scheme. First, one trains the often neural network-based reconstruction s
Externí odkaz:
http://arxiv.org/abs/2306.01335
Autor:
Tanyu, Derick Nganyu, Ning, Jianfeng, Freudenberg, Tom, Heilenkötter, Nick, Rademacher, Andreas, Iben, Uwe, Maass, Peter
Publikováno v:
Inverse Problems (2023)
Recent years have witnessed a growth in mathematics for deep learning--which seeks a deeper understanding of the concepts of deep learning with mathematics and explores how to make it more robust--and deep learning for mathematics, where deep learnin
Externí odkaz:
http://arxiv.org/abs/2212.03130
Autor:
Arrastia, Jean Le'Clerc, Heilenkötter, Nick, Baguer, Daniel Otero, Hauberg-Lotte, Lena, Boskamp, Tobias, Hetzer, Sonja, Duschner, Nicole, Schaller, Jörg, Maaß, Peter
Accurate and fast assessment of resection margins is an essential part of a dermatopathologist's clinical routine. In this work, we successfully develop a deep learning method to assist the pathologists by marking critical regions that have a high pr
Externí odkaz:
http://arxiv.org/abs/2103.03759
Akademický článek
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Akademický článek
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Autor:
Le'Clerc Arrastia, Jean, Heilenkötter, Nick, Otero Baguer, Daniel, Hauberg-Lotte, Lena, Boskamp, Tobias, Hetzer, Sonja, Duschner, Nicole, Schaller, Jörg, Maass, Peter
Publikováno v:
Journal of Imaging; Apr2021, Vol. 7 Issue 4, p1-15, 15p
