Zobrazeno 1 - 10
of 870
pro vyhledávání: '"Damek A"'
Autor:
Damek, Ewa, Mentemeier, Sebastian
In recent works on the theory of machine learning, it has been observed that heavy tail properties of Stochastic Gradient Descent (SGD) can be studied in the probabilistic framework of stochastic recursions. In particular, G\"{u}rb\"{u}zbalaban et al
Externí odkaz:
http://arxiv.org/abs/2403.13868
We demonstrate that in situ coherent diffractive imaging (CDI), which harnesses the coherent interference between a strong and a weak beam illuminating a static and dynamic structure, can be a very dose-efficient imaging method. At low doses, in situ
Externí odkaz:
http://arxiv.org/abs/2306.11283
Modern machine learning paradigms, such as deep learning, occur in or close to the interpolation regime, wherein the number of model parameters is much larger than the number of data samples. In this work, we propose a regularity condition within the
Externí odkaz:
http://arxiv.org/abs/2306.02601
In their seminal work, Polyak and Juditsky showed that stochastic approximation algorithms for solving smooth equations enjoy a central limit theorem. Moreover, it has since been argued that the asymptotic covariance of the method is best possible am
Externí odkaz:
http://arxiv.org/abs/2301.06632
Autor:
Adrian Damek, Lars Kurch, Friedrich Christian Franke, Andishe Attarbaschi, Auke Beishuizen, Michaela Cepelova, Francesco Ceppi, Stephen Daw, Karin Dieckmann, Ana Fernández-Teijeiro, Tobias Feuchtinger, Jamie E. Flerlage, Alexander Fosså, Thomas W. Georgi, Dirk Hasenclever, Andrea Hraskova, Jonas Karlen, Tomasz Klekawka, Regine Kluge, Dieter Körholz, Judith Landman-Parker, Thierry Leblanc, Christine Mauz-Körholz, Markus Metzler, Jane Pears, Jonas Steglich, Anne Uyttebroeck, Dirk Vordermark, William Hamish Wallace, Walter Alexander Wohlgemuth, Dietrich Stoevesandt
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-9 (2024)
Abstract Hypodense volumes (HDV) in mediastinal masses can be visualized in a computed tomography scan in Hodgkin lymphoma. We analyzed staging CT scans of 1178 patients with mediastinal involvement from the EuroNet-PHL-C1 trial and explored correlat
Externí odkaz:
https://doaj.org/article/56fe25c1577a4d6ebc0c24ea6b216610
Autor:
Davis, Damek, Jiang, Tao
We analyze a preconditioned subgradient method for optimizing composite functions $h \circ c$, where $h$ is a locally Lipschitz function and $c$ is a smooth nonlinear mapping. We prove that when $c$ satisfies a constant rank property and $h$ is semis
Externí odkaz:
http://arxiv.org/abs/2212.13278
We consider a strictly stationary random field on the two-dimensional integer lattice with regularly varying marginal and finite-dimensional distributions. Exploiting the regular variation, we define the spatial extremogram which takes into account o
Externí odkaz:
http://arxiv.org/abs/2211.03260
Autor:
Davis, Damek1 (AUTHOR), Drusvyatskiy, Dmitriy2 (AUTHOR) ddrusv@uw.edu, Charisopoulos, Vasileios1 (AUTHOR)
Publikováno v:
Mathematical Programming. Sep2024, Vol. 207 Issue 1/2, p145-190. 46p.
Workplaces of the future require advanced competence profiles from employees, not least due to new options for teleworking and new complex digital tools. The acquisition of advanced competence profiles is to be addressed by formal education. For exam
Externí odkaz:
http://arxiv.org/abs/2207.01498
Autor:
Davis, Damek, Jiang, Liwei
Classical results show that gradient descent converges linearly to minimizers of smooth strongly convex functions. A natural question is whether there exists a locally nearly linearly convergent method for nonsmooth functions with quadratic growth. T
Externí odkaz:
http://arxiv.org/abs/2205.00064