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of 16 467
pro vyhledávání: '"Tarnawski A"'
Autor:
Tomasz Pudłocki
Publikováno v:
Law: Journal of the University of Latvia, Vol 16 (2023)
The article considers the problem of reaction of the greatest Polish opposition party to the post-1926 “Sanation”, namely, the right-wing National Democrat attitudes to the ideas of changes of the Polish Constitution of 1921. The author focuses o
Externí odkaz:
https://doaj.org/article/89789e70c4a04f5f931a9b48b8465f3a
Autor:
Migała, Mariusz1 m.migala@po.edu.pl, Jandziś, Sławomir2
Publikováno v:
Acta Medico-Historica Adriatica. 2020, Vol. 18 Issue 2, p273-290. 18p.
Akademický článek
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Autor:
Budrewicz, Aleksandra
Publikováno v:
Prace Polonistyczne / Studies in Polish Literature. (77):55-77
Externí odkaz:
https://www.ceeol.com/search/article-detail?id=1086179
Autor:
Tarkowska, Natalia
Publikováno v:
Krakowskie Pismo Kresowe / Cracovian Kresy’s Journal. (10):121-150
Externí odkaz:
https://www.ceeol.com/search/article-detail?id=780112
Publikováno v:
Yearbook of Conrad Studies, 2014 Jan 01. 9, 75-87.
Externí odkaz:
https://www.jstor.org/stable/44782447
Distributed execution of deep learning training involves a dynamic interplay between hardware accelerator architecture and device placement strategy. This is the first work to explore the co-optimization of determining the optimal architecture and de
Externí odkaz:
http://arxiv.org/abs/2407.13143
Autor:
Borkowski, Robert
Publikováno v:
Bezpieczeństwo. Teoria i Praktyka / Security. Theory and Practice. XXXI(2):171-176
Externí odkaz:
https://www.ceeol.com/search/article-detail?id=734897
Many methods in differentially private model training rely on computing the similarity between a query point (such as public or synthetic data) and private data. We abstract out this common subroutine and study the following fundamental algorithmic p
Externí odkaz:
http://arxiv.org/abs/2403.08917
Autor:
Götz, Michael, Weber, Christian, Binczyk, Franciszek, Polanska, Joanna, Tarnawski, Rafal, Bobek-Billewicz, Barbara, Köthe, Ullrich, Kleesiek, Jens, Stieltjes, Bram, Maier-Hein, Klaus H.
Publikováno v:
IEEE Transactions on Medical Imaging ( Volume: 35, Issue: 1, January 2016)
We propose a new method that employs transfer learning techniques to effectively correct sampling selection errors introduced by sparse annotations during supervised learning for automated tumor segmentation. The practicality of current learning-base
Externí odkaz:
http://arxiv.org/abs/2403.07434