Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Petra Vidnerová"'
Autor:
Petra Vidnerová, Roman Neruda
Publikováno v:
Modelling, Vol 2, Iss 35, Pp 659-674 (2021)
Modelling
Volume 2
Issue 4
Pages 35-674
Modelling
Volume 2
Issue 4
Pages 35-674
Precise environmental modelling of pollutants distributions represents a key factor for addresing the issue of urban air pollution. Nowadays, urban air pollution monitoring is primarily carried out by employing sparse networks of spatially distribute
Autor:
Levínský R, Ludek Berec, Josef Šlerka, Kuběna A, Milan Zajicek, Martin Smid, Roman Neruda, Tuček, Jan Trnka, Zapletal F, Gabriela Suchoparova, Tomas Diviak, Petra Vidnerová
This report presents a technical description of our agent-based epidemic model of a particular middle-sized municipality. We have developed a realistic model with 56 thousand inhabitants and 2.7 millions of social contacts. These form a multi-layer s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4bad85526efc970ceeeae764cd948589
https://doi.org/10.1101/2021.05.13.21257139
https://doi.org/10.1101/2021.05.13.21257139
Publikováno v:
International journal of neural systems. 31(10)
Metalearning, an important part of artificial intelligence, represents a promising approach for the task of automatic selection of appropriate methods or algorithms. This paper is interested in recommending a suitable estimator for nonlinear regressi
Publikováno v:
Analytical Methods in Statistics ISBN: 9783030488130
The methodology of automatic method selection (metalearning) allows to recommend the most suitable method (e.g. algorithm or statistical estimator) from several alternatives for a given dataset, based on information learned over a training database o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5688a30e5ac4cce1c5807fdca92c778a
https://doi.org/10.1007/978-3-030-48814-7_7
https://doi.org/10.1007/978-3-030-48814-7_7
Autor:
Jan Kalina, Petra Vidnerová
Publikováno v:
Functional and High-Dimensional Statistics and Related Fields ISBN: 9783030477554
Estimation, prediction or smoothing of curves represents a fundamental task of functional data analysis. Nonlinear regression methods allow to search for the best-fit curves explaining the dependence of a response variable on available independent va
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d8dca978ab2735eade6d0503c4908c53
https://doi.org/10.1007/978-3-030-47756-1_20
https://doi.org/10.1007/978-3-030-47756-1_20
Autor:
Petra Vidnerová, Roman Neruda
Publikováno v:
Neural Information Processing ISBN: 9783030638351
ICONIP (3)
ICONIP (3)
In this paper, we propose a multi-objective evolutionary algorithm for automatic deep neural architecture search. The algorithm optimizes the performance of the model together with the number of network parameters. This allows exploring architectures
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d9528e0cb0daead0a5a4fc08552e512
https://doi.org/10.1007/978-3-030-63836-8_23
https://doi.org/10.1007/978-3-030-63836-8_23
Autor:
Petra Vidnerová, Jan Kalina
Publikováno v:
Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference ISBN: 9783030487904
EANN
EANN
Common types of artificial neural networks have been well known to suffer from the presence of outlying measurements (outliers) in the data. However, there are only a few available robust alternatives for training common form of neural networks. In t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7b5e8e58333091f108366811fe9c74e7
https://doi.org/10.1007/978-3-030-48791-1_43
https://doi.org/10.1007/978-3-030-48791-1_43
Publikováno v:
Artificial Intelligence and Soft Computing ISBN: 9783030614003
ICAISC (1)
ICAISC (1)
The choice of an architecture is crucial for the performance of the neural network, and thus automatic methods for architecture search have been proposed to provide a data-dependent solution to this problem. In this paper, we deal with an automatic n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1d0deaf599890f75ed9971ff4284f7aa
https://doi.org/10.1007/978-3-030-61401-0_25
https://doi.org/10.1007/978-3-030-61401-0_25
Autor:
Jan Kalina, Petra Vidnerová
Publikováno v:
Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference ISBN: 9783030487904
EANN
EANN
Metalearning is a methodology aiming at recommending the most suitable algorithm (or method) from several alternatives for a particular dataset. Its classification rule is learned over an available training database of datasets. It gradually penetrat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fff24f4aad17a6b7d29112e65f7076a2
https://doi.org/10.1007/978-3-030-48791-1_39
https://doi.org/10.1007/978-3-030-48791-1_39
Autor:
Roman Neruda, Petra Vidnerová
Publikováno v:
Neural networks : the official journal of the International Neural Network Society. 127
This paper deals with the vulnerability of machine learning models to adversarial examples and its implication for robustness and generalization properties. We propose an evolutionary algorithm that can generate adversarial examples for any machine l