Zobrazeno 1 - 10
of 2 399
pro vyhledávání: '"Papež, P."'
Deep generative models have recently made a remarkable progress in capturing complex probability distributions over graphs. However, they are intractable and thus unable to answer even the most basic probabilistic inference queries without resorting
Externí odkaz:
http://arxiv.org/abs/2408.09451
Daily internet communication relies heavily on tree-structured graphs, embodied by popular data formats such as XML and JSON. However, many recent generative (probabilistic) models utilize neural networks to learn a probability distribution over undi
Externí odkaz:
http://arxiv.org/abs/2408.07394
In this work, we develop algebraic solvers for linear systems arising from the discretization of second-order elliptic problems by saddle-point mixed finite element methods of arbitrary polynomial degree $p \ge 0$. We present a multigrid and a two-le
Externí odkaz:
http://arxiv.org/abs/2406.09872
Multilevel methods represent a powerful approach in numerical solution of partial differential equations. The multilevel structure can also be used to construct estimates for total and algebraic errors of computed approximations. This paper deals wit
Externí odkaz:
http://arxiv.org/abs/2405.06532
Autor:
Papež, Jan, Tichý, Petr
In [Meurant, Pape\v{z}, Tich\'y; Numerical Algorithms 88, 2021], we presented an adaptive estimate for the energy norm of the error in the conjugate gradient (CG) method. In this paper, we extend the estimate to algorithms for solving linear approxim
Externí odkaz:
http://arxiv.org/abs/2305.02044
Publikováno v:
Diagnostics, Vol 14, Iss 17, p 1982 (2024)
This prospective study aimed to determine the impact of Fascial Manipulation® by Stecco (FM) on the range of motion (ROM) of internal rotation (IR) and horizontal adduction (HADD) in asymptomatic handball players, representing significant risk facto
Externí odkaz:
https://doaj.org/article/b20b37999e2f47d6b72e11a1848e1adc
Autor:
Rashid Dallaev, Ranjini Sarkar, Daud Selimov, Nikola Papež, Pavla Kočková, Richard Schubert, Klara Častková, Farid Orudzhev, Shikhgasan Ramazanov, Vladimír Holcman
Publikováno v:
Polymers, Vol 16, Iss 17, p 2412 (2024)
Nitride salts were added to polyvinylidene fluoride fibers and then the fiber mats were prepared by electrospinning. An experimental investigation of the structure was provided by Raman, FTIR, SEM, and XRD. The phase ratio of the polymer was studied
Externí odkaz:
https://doaj.org/article/33c0b8f3c546443bb9f5b4d13d2f9320
Traditional methods for unsupervised learning of finite mixture models require to evaluate the likelihood of all components of the mixture. This becomes computationally prohibitive when the number of components is large, as it is, for example, in the
Externí odkaz:
http://arxiv.org/abs/2110.04776
Autor:
Jan Papež, René Labounek, Petr Jabandžiev, Katarína Česká, Kateřina Slabá, Hana Ošlejšková, Štefania Aulická, Igor Nestrašil
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract Our goal was to identify highly accurate empirical models for the prediction of the risk of febrile seizure (FS) and FS recurrence. In a prospective, three-arm, case–control study, we enrolled 162 children (age 25.8 ± 17.1 months old, 71
Externí odkaz:
https://doaj.org/article/b646168095534c8881b5ff1f762700a0
Autor:
Papež, Milan, Quinn, Anthony
Bayesian transfer learning (BTL) is defined in this paper as the task of conditioning a target probability distribution on a transferred source distribution. The target globally models the interaction between the source and target, and conditions on
Externí odkaz:
http://arxiv.org/abs/2101.06884