Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Petr Tichavský"'
Autor:
Anh Huy Phan, Konstantin Sozykin, Julia Gusak, Dmitry Ermilov, Andrzej Cichocki, Petr Tichavský, Valeriy Glukhov, Konstantin Sobolev, Ivan V. Oseledets
Publikováno v:
Computer Vision – ECCV 2020 ISBN: 9783030585259
ECCV (29)
ECCV (29)
Most state-of-the-art deep neural networks are overparameterized and exhibit a high computational cost. A straightforward approach to this problem is to replace convolutional kernels with its low-rank tensor approximations, whereas the Canonical Poly
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b147a4f6bfa71e897e90862d8d370e62
https://doi.org/10.1007/978-3-030-58526-6_31
https://doi.org/10.1007/978-3-030-58526-6_31
Publikováno v:
Signal Processing. 138:313-320
Tensor diagonalization means transforming a given tensor to an exactly or nearly diagonal form through multiplying the tensor by non-orthogonal invertible matrices along selected dimensions of the tensor. It is generalization of approximate joint dia
Publikováno v:
Latent Variable Analysis and Signal Separation ISBN: 9783319937632
LVA/ICA
LVA/ICA
We propose a new algorithm for Independent Component Extraction that extracts one non-Gaussian component and is capable to exploit the non-Gaussianity of background signals without decomposing them into independent components. The algorithm is suitab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d92331b1843aad3141b1d571c17331a
https://doi.org/10.1007/978-3-319-93764-9_16
https://doi.org/10.1007/978-3-319-93764-9_16
This book constitutes the proceedings of the 13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017, held in Grenoble, France, in Feburary 2017. The 53 papers presented in this volume were carefully reviewed and
Publikováno v:
Latent Variable Analysis and Signal Separation ISBN: 9783319535463
LVA/ICA
LVA/ICA
This paper deals with estimation of structured signals such as damped sinusoids, exponentials, polynomials, and their products from single channel data. It is shown that building tensors from this kind of data results in tensors with hidden block str
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a5e2cf6bf21b7732fedbff92dcde2de9
https://doi.org/10.1007/978-3-319-53547-0_4
https://doi.org/10.1007/978-3-319-53547-0_4
Publikováno v:
Latent Variable Analysis and Signal Separation ISBN: 9783319535463
LVA/ICA
LVA/ICA
In many applications, there is a need to blindly separate independent sources from their linear instantaneous mixtures while the mixing matrix or source properties are slowly or abruptly changing in time. The easiest way to separate the data is to co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e35673b9549135b5df313a8e9c42228d
https://doi.org/10.1007/978-3-319-53547-0_17
https://doi.org/10.1007/978-3-319-53547-0_17
Autor:
Petr Tichavský, Jiří Vomlel
Publikováno v:
International Journal of Approximate Reasoning. 55:1072-1092
The specification of conditional probability tables (CPTs) is a difficult task in the construction of probabilistic graphical models. Several types of canonical models have been proposed to ease that difficulty. Noisy-threshold models generalize the
This book constitutes the proceedings of the 12th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICS 2015, held in Liberec, Czech Republic, in August 2015. The 61 revised full papers presented – 29 accepted as oral
Autor:
Traian Abrudan, Ella Bingham, Guangyong Chen, KyungHyun Cho, Scott C. Douglas, Markku Hauta-Kasari, Pheng Ann Heng, Aapo Hyvärinen, Satoru Ishikawa, Heikki Kälviäinen, Juha Karhunen, Irwin King, Visa Koivunen, Zbyněk Koldovský, Markus Koskela, Jorma Laaksonen, Hannu Laamanen, Heikki Mannila, Jussi Parkkinen, Matti Pietikäinen, Tapani Raiko, Mats Sjöberg, Petr Tichavský, Harri Valpola, Ricardo Vigário, Ville Viitaniemi, Lei Xu, Zhirong Yang, Guoying Zhao, Fengyuan Zhu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bbf18d8da39f06405a7c865705944d36
https://doi.org/10.1016/b978-0-12-802806-3.09992-9
https://doi.org/10.1016/b978-0-12-802806-3.09992-9
Autor:
Zbynvěk Koldovský, Petr Tichavský
The article presents a survey of improved variants of the famous FastICA algorithm for Independent Component Analysis. Variants of the algorithm tailored to separate mixtures of stationary non-Gaussian signals and mixtures of nonstationary (block-wis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd945b950f342e7cb7027f4f7e58de5a
https://doi.org/10.1016/b978-0-12-802806-3.00002-6
https://doi.org/10.1016/b978-0-12-802806-3.00002-6