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pro vyhledávání: '"Fojtík, Vít"'
The unwavering success of deep learning in the past decade led to the increasing prevalence of deep learning methods in various application fields. However, the downsides of deep learning, most prominently its lack of trustworthiness, may not be comp
Externí odkaz:
http://arxiv.org/abs/2408.06212
Publikováno v:
Journal of Computational and Graphical Statistics, 33(2), 699-713, 2023
The Tukey (or halfspace) depth extends nonparametric methods toward multivariate data. The multivariate analogues of the quantiles are the central regions of the Tukey depth, defined as sets of points in the $d$-dimensional space whose Tukey depth ex
Externí odkaz:
http://arxiv.org/abs/2208.04587
Akademický článek
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Autor:
Fojtík, Vít1 (AUTHOR), Laketa, Petra1 (AUTHOR), Mozharovskyi, Pavlo2 (AUTHOR), Nagy, Stanislav1 (AUTHOR) nagy@karlin.mff.cuni.cz
Publikováno v:
Journal of Computational & Graphical Statistics. Apr-Jun2024, Vol. 33 Issue 2, p699-713. 15p.
Autor:
Fojtík, Vít
Despite abundant research into neural network applications, many areas of the under- lying mathematics remain largely unexplored. The study of neural network expressivity is vital for understanding their capabilities and limitations. However, even fo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2186::d1e976dfd17c3b2095ccf846f40550fa
http://www.nusl.cz/ntk/nusl-507491
http://www.nusl.cz/ntk/nusl-507491
Autor:
Fojtík, Vít
The aim of this thesis is to study methods of constructing lower bounds on Boolean formula size. We focus mainly on formal complexity measures, gener- alizing the well-known Krapchenko measure to a class of graph measures, which we thereafter study.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2186::5122f49c09064143bdb121a0d4b228c5
http://www.nusl.cz/ntk/nusl-398240
http://www.nusl.cz/ntk/nusl-398240
Autor:
Fojtík, Vít
The aim of this thesis is to compare two major models of random sets, the well established random closed sets (RACS) and the more recent and more general random measurable sets (RAMS). First, we study the topologies underlying the models, showing the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2186::c8fd9580fd3eed17b8b354ec6b68548b
http://www.nusl.cz/ntk/nusl-405508
http://www.nusl.cz/ntk/nusl-405508