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- 1.0572576 - ÚI 2024 RIV GB eng J - Journal Article
Kůrková, Věra - Sanguineti, M.
Approximation of Classifiers by Deep Perceptron Networks.
Neural Networks. Roč. 165, August 2023 (2023), s. 654-661. ISSN 0893-6080. E-ISSN 1879-2782
R&D Projects: GA ČR(CZ) GA22-02067S
Institutional support: RVO:67985807
Keywords : Approximation by deep networks * Probabilistic bounds on approximation errors * Random classifiers * Concentration of measure * Method of bounded differences * Growth functions
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 7.8, year: 2022
Method of publishing: Limited access
https://dx.doi.org/10.1016/j.neunet.2023.06.004
Permanent Link: https://hdl.handle.net/11104/0343221 - 2.0521198 - ÚI 2021 RIV CH eng M - Monography Chapter
Kůrková, Věra
Limitations of Shallow Networks.
Recent Trends in Learning from Data. Cham: Springer, 2020 - (Oneto, L.; Navarin, N.; Sperduti, A.; Anguita, D.), s. 129-154. Studies in Computational Intelligence, 896. ISBN 978-3-030-43882-1
R&D Projects: GA ČR(CZ) GA18-23827S
Institutional support: RVO:67985807
Keywords : shallow and deep networks * model complexity * probabilistic lower bounds
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Permanent Link: http://hdl.handle.net/11104/0307155 - 3.0512092 - ÚI 2020 RIV DE eng C - Conference Paper (international conference)
Tumpach, J. - Krčál, M. - Holeňa, Martin
Deep networks in online malware detection.
ITAT 2019: Information Technologies – Applications and Theory. Aachen: Technical University & CreateSpace Independent Publishing, 2019 - (Barančíková, P.; Holeňa, M.; Horváth, T.; Pleva, M.; Rosa, R.), s. 90-98. CEUR Workshop Proceeding, 2473. ISSN 1613-0073.
[ITAT 2019: Conference Information Technologies - Applications and Theory /19./. Donovaly (SK), 20.09.2019-24.09.2019]
R&D Projects: GA ČR(CZ) GA18-18080S
Grant - others:GA MŠk(CZ) LM2015042
Institutional support: RVO:67985807
Keywords : artificial neural networks * multilayer perceptrons * deep networks * semi-supervised learning * malware detection
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-2473/paper7.pdf
Permanent Link: http://hdl.handle.net/11104/0302298File Download Size Commentary Version Access 0512092-aoa.pdf 3 581.3 KB OpenAccess Publisher’s postprint open-access - 4.0512089 - ÚI 2020 RIV DE eng C - Conference Paper (international conference)
Fanta, M. - Pulc, P. - Holeňa, Martin
Rules extraction from neural networks trained on multimedia data.
ITAT 2019: Information Technologies – Applications and Theory. Aachen: Technical University & CreateSpace Independent Publishing, 2019 - (Barančíková, P.; Holeňa, M.; Horváth, T.; Pleva, M.; Rosa, R.), s. 26-35. CEUR Workshop Proceeding, 2473. ISSN 1613-0073.
[ITAT 2019: Conference Information Technologies - Applications and Theory /19./. Donovaly (SK), 20.09.2019-24.09.2019]
R&D Projects: GA ČR(CZ) GA18-18080S
Institutional support: RVO:67985807
Keywords : artificial neural networks * multilayer perceptrons * deep networks * rules extraction * multimedia data
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-2473/paper4.pdf
Permanent Link: http://hdl.handle.net/11104/0302294File Download Size Commentary Version Access 0512089-aoa.pdf 4 439.9 KB OpenAccess Publisher’s postprint open-access - 5.0500123 - ÚI 2019 CZ eng V - Research Report
Křen, Tomáš
Transforming hierarchical images to program expressions using deep networks.
Prague: ICS CAS, 2018. 12 s. Technical report, V-1263.
R&D Projects: GA ČR(CZ) GA18-23827S
Institutional support: RVO:67985807
Keywords : deep networks * automatic program synthesis * image processing
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Permanent Link: http://hdl.handle.net/11104/0292265File Download Size Commentary Version Access 0500123-v-1263.pdf 26 1.3 MB Other open-access - 6.0493825 - ÚI 2019 RIV CH eng C - Conference Paper (international conference)
Kůrková, Věra
Sparsity and Complexity of Networks Computing Highly-Varying Functions.
Artificial Neural Networks and Machine Learning – ICANN 2018. Proceedings, Part III. Cham: Springer, 2018 - (Kůrková, V.; Manolopoulos, Y.; Hammer, B.; Iliadis, L.; Maglogiannis, I.), s. 534-543. Lecture Notes in Computer Science, 11141. ISBN 978-3-030-01423-0. ISSN 0302-9743.
[ICANN 2018. International Conference on Artificial Neural Networks /27./. Rhodes (GR), 04.10.2018-07.10.2018]
R&D Projects: GA ČR(CZ) GA18-23827S
Institutional support: RVO:67985807
Keywords : Shallow and deep networks * Model complexity * Sparsity * Highly-varying functions * Covering numbers * Dictionaries of computational units * Perceptrons
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://www.springer.com/us/book/9783030014230
Permanent Link: http://hdl.handle.net/11104/0287121File Download Size Commentary Version Access a0493825.pdf 4 377.7 KB Author’s postprint require - 7.0485613 - ÚI 2020 RIV US eng J - Journal Article
Kůrková, Věra
Limitations of Shallow Networks Representing Finite Mappings.
Neural Computing & Applications. Roč. 31, č. 6 (2019), s. 1783-1792. ISSN 0941-0643. E-ISSN 1433-3058
R&D Projects: GA ČR GA15-18108S; GA ČR(CZ) GA18-23827S
Institutional support: RVO:67985807
Keywords : shallow and deep networks * sparsity * variational norms * functions on large finite domains * finite dictionaries of computational units * pseudo-noise sequences * perceptron networks
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 4.774, year: 2019
Method of publishing: Open access
http://dx.doi.org/10.1007/s00521-018-3680-1
Permanent Link: http://hdl.handle.net/11104/0280569File Download Size Commentary Version Access 0485613-afin.pdf 12 608 KB stránkovaná, finální verze Publisher’s postprint require 0485613.pdf 5 330.3 KB Author´s preprint require - 8.0474092 - ÚI 2019 RIV US eng J - Journal Article
Kůrková, Věra
Constructive Lower Bounds on Model Complexity of Shallow Perceptron Networks.
Neural Computing & Applications. Roč. 29, č. 7 (2018), s. 305-315. ISSN 0941-0643. E-ISSN 1433-3058
R&D Projects: GA ČR GA15-18108S
Institutional support: RVO:67985807
Keywords : shallow and deep networks * model complexity and sparsity * signum perceptron networks * finite mappings * variational norms * Hadamard matrices
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 4.664, year: 2018
Permanent Link: http://hdl.handle.net/11104/0271209File Download Size Commentary Version Access a0474092.pdf 8 495.8 KB Publisher’s postprint require