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  1. 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. 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. 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/11104/0302298
    FileDownloadSizeCommentaryVersionAccess
    0512092-aoa.pdf3581.3 KBOpenAccessPublisher’s postprintopen-access
     
     
  4. 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/11104/0302294
    FileDownloadSizeCommentaryVersionAccess
    0512089-aoa.pdf4439.9 KBOpenAccessPublisher’s postprintopen-access
     
     
  5. 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/11104/0292265
    FileDownloadSizeCommentaryVersionAccess
    0500123-v-1263.pdf261.3 MBOtheropen-access
     
     
  6. 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/11104/0287121
    FileDownloadSizeCommentaryVersionAccess
    a0493825.pdf4377.7 KBAuthor’s postprintrequire
     
     
  7. 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/11104/0280569
    FileDownloadSizeCommentaryVersionAccess
    0485613-afin.pdf12608 KBstránkovaná, finální verzePublisher’s postprintrequire
    0485613.pdf5330.3 KBAuthor´s preprintrequire
     
     
  8. 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/11104/0271209
    FileDownloadSizeCommentaryVersionAccess
    a0474092.pdf8495.8 KBPublisher’s postprintrequire
     
     


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