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- 1.0507969 - ÚI 2020 RIV CH eng C - Conference Paper (international conference)
Kůrková, Věra
Probabilistic Bounds for Approximation by Neural Networks.
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. Proceedings, Part I. Cham: Springer, 2019 - (Tetko, I.; Kůrková, V.; Karpov, P.; Theis, F.), s. 418-428. Lecture Notes in Computer Science, 11727. ISBN 978-3-030-30486-7. ISSN 0302-9743.
[ICANN 2019. International Conference on Artificial Neural Networks /28./. Munich (DE), 17.09.2019-19.09.2019]
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional support: RVO:67985807
Keywords : Approximation of random functions * Feedforward networks * Dictionaries of computational units * High-dimensional geometry * Concentration of measure * Azuma-Hoeffding inequalities
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/11104/0298932 - 2.0493926 - ÚI 2019 RIV DE eng C - Conference Paper (international conference)
Kůrková, Věra - Sanguineti, M.
Probabilistic Bounds on Complexity of Networks Computing Binary Classification Tasks.
ITAT 2018: Information Technologies – Applications and Theory. Proceedings of the 18th conference ITAT 2018. Aachen: Technical University & CreateSpace Independent Publishing Platform, 2018 - (Krajči, S.), s. 86-91. CEUR Workshop Proceedings, V-2203. ISSN 1613-0073.
[ITAT 2018. Conference on Information Technologies – Applications and Theory /18./. Plejsy (SK), 21.09.2018-25.09.2018]
R&D Projects: GA ČR(CZ) GA18-23827S
Institutional support: RVO:67985807
Keywords : feedforward networks * binary classification * measures of sparsity * probabilistic bounds * dictionaries of computational units
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-2203/86.pdf
Permanent Link: http://hdl.handle.net/11104/0287193File Download Size Commentary Version Access a0493926.pdf 5 627.8 KB Publisher’s postprint require - 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 - 4.0485613 - 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 - 5.0485611 - ÚI 2020 RIV US eng J - Journal Article
Kůrková, Věra - Sanguineti, M.
Classification by Sparse Neural Networks.
IEEE Transactions on Neural Networks and Learning Systems. Roč. 30, č. 9 (2019), s. 2746-2754. ISSN 2162-237X. E-ISSN 2162-2388
R&D Projects: GA ČR GA15-18108S; GA ČR(CZ) GA18-23827S
Institutional support: RVO:67985807
Keywords : Binary classification * Chernoff–Hoeffding bound * dictionaries of computational units * feedforward networks * measures of sparsity
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 8.793, year: 2019
Method of publishing: Limited access
http://dx.doi.org/10.1109/TNNLS.2018.2888517
Permanent Link: http://hdl.handle.net/11104/0280566File Download Size Commentary Version Access 0485611-a.pdf 18 458.9 KB Publisher’s postprint require