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- 1.0512092 - 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 - 2.0512089 - 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 - 3.0493825 - 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 - 4.0485562 - ÚI 2021 RIV CH eng M - Monography Chapter
Kůrková, Věra - Kainen, P.C.
Integral Transforms Induced by Heaviside Perceptrons.
Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy, etc. Methods and Their Applications. Cham: Springer, 2020 - (Kosheleva, O.; Shary, S.; Xiang, G.; Zapatrin, R.), s. 631-649. Studies in Computational Intelligence, 835. ISBN 978-3-030-31040-0
R&D Projects: GA ČR GA15-18108S; GA ČR(CZ) GA18-23827S
Institutional support: RVO:67985807
Keywords : representations of functions by neural networks * Haeviside perceptrons * integral transforms
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/11104/0280525 - 5.0476509 - ÚI 2018 RIV CH eng C - Conference Paper (international conference)
Kůrková, Věra
Sparsity of Shallow Networks Representing Finite Mappings.
EANN 2017. Cham: Springer, 2017 - (Boracchi, G.; Iliadis, L.; Jayne, C.; Likas, A.), s. 337-348. Communications in Computer and Information Science, 744. ISBN 978-3-319-65171-2. ISSN 1865-0929.
[EANN 2017. International Conference /18./. Athens (GR), 25.08.2017-27.08.2017]
R&D Projects: GA ČR GA15-18108S
Institutional support: RVO:67985807
Keywords : shallow networks * finite mappings * sparsity * model complexity * concentration of measure * signum perceptrons
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/11104/0272989File Download Size Commentary Version Access a0476509.pdf 3 229.6 KB Publisher’s postprint require - 6.0473964 - ÚI 2018 RIV GB eng J - Journal Article
Kůrková, Věra - Sanguineti, M.
Probabilistic Lower Bounds for Approximation by Shallow Perceptron Networks.
Neural Networks. Roč. 91, July (2017), s. 34-41. ISSN 0893-6080. E-ISSN 1879-2782
R&D Projects: GA ČR GA15-18108S
Institutional support: RVO:67985807
Keywords : shallow networks * perceptrons * model complexity * lower bounds on approximation rates * Chernoff-Hoeffding bounds
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 7.197, year: 2017
Permanent Link: http://hdl.handle.net/11104/0271067File Download Size Commentary Version Access a0473964.pdf 16 549.9 KB Publisher’s postprint require - 7.0460704 - ÚI 2017 RIV CH eng C - Conference Paper (international conference)
Kůrková, Věra
Lower Bounds on Complexity of Shallow Perceptron Networks.
Engineering Applications of Neural Networks. Cham: Springer, 2016 - (Jayne, C.; Iliadis, L.), s. 283-294. Communications in Computer and Information Science, 629. ISBN 978-3-319-44187-0. ISSN 1865-0929.
[EANN 2016. International Conference /17./. Aberdeen (GB), 02.09.2016-05.09.2016]
R&D Projects: GA ČR GA15-18108S
Institutional support: RVO:67985807
Keywords : shallow feedforward networks * signum perceptrons * finite mappings * model complexity * Hadamard matrices
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/11104/0260719File Download Size Commentary Version Access a0460704.pdf 3 196.9 KB Publisher’s postprint require - 8.0446410 - ÚI 2016 RIV NL eng J - Journal Article
Kůrková, Věra - Sanguineti, M.
Model Complexities of Shallow Networks Representing Highly Varying Functions.
Neurocomputing. Roč. 171, 1 January (2016), s. 598-604. ISSN 0925-2312. E-ISSN 1872-8286
R&D Projects: GA MŠMT(CZ) LD13002
Grant - others:grant for Visiting Professors(IT) GNAMPA-INdAM
Institutional support: RVO:67985807
Keywords : shallow networks * model complexity * highly varying functions * Chernoff bound * perceptrons * Gaussian kernel units
Subject RIV: IN - Informatics, Computer Science
Impact factor: 3.317, year: 2016
Permanent Link: http://hdl.handle.net/11104/0248405File Download Size Commentary Version Access a0446410.pdf 23 393.9 KB Publisher’s postprint require - 9.0443724 - ÚI 2016 RIV CH eng C - Conference Paper (international conference)
Kůrková, Věra
Complexity of Shallow Networks Representing Finite Mappings.
Artificial Intelligence and Soft Computing. Vol. 1. Cham: Springer, 2015 - (Rutkowski, L.; Korytkowski, M.; Scherer, R.; Tadeusiewicz, R.; Zadeh, L.; Zurada, J.), s. 39-48. Lecture Notes in Artificial Intelligence, 9119. ISBN 978-3-319-19323-6. ISSN 0302-9743.
[ICAISC 2015. International Conference on Artificial Intelligence and Soft Computing /14./. Zakopane (PL), 12.06.2015-16.06.2015]
R&D Projects: GA ČR GA15-18108S
Institutional support: RVO:67985807
Keywords : Shallow feedforward networks * Signum perceptrons * Finite mappings * Model complexity * Hadamard matrices
Subject RIV: IN - Informatics, Computer Science
Permanent Link: http://hdl.handle.net/11104/0246406File Download Size Commentary Version Access a0443724.pdf 3 217.2 KB Publisher’s postprint require - 10.0430374 - ÚI 2015 RIV CH eng C - Conference Paper (international conference)
Kůrková, Věra - Sanguineti, M.
Complexity of Shallow Networks Representing Functions with Large Variations.
Artificial Neural Networks and Machine Learning - ICANN 2014. Cham: Springer, 2014 - (Wermter, S.; Weber, C.; Duch, W.; Honkela, T.; Koprinkova-Hristova, P.; Magg, S.; Palm, G.; Villa, A.), s. 331-338. Lecture Notes in Computer Science, 8681. ISBN 978-3-319-11178-0.
[ICANN 2014. International Conference on Artificial Neural Networks /24./. Hamburg (DE), 15.09.2014-19.09.2014]
R&D Projects: GA MŠMT(CZ) LD13002
Institutional support: RVO:67985807
Keywords : one-hidden-layer networks * model complexity * representations of multivariable functions * perceptrons * Gaussian SVMs
Subject RIV: IN - Informatics, Computer Science
Permanent Link: http://hdl.handle.net/11104/0235318File Download Size Commentary Version Access a0430374.pdf 0 203.8 KB Publisher’s postprint require