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- 1.0521198 - 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 - 2.0493825 - 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 - 3.0476509 - 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 - 4.0474092 - 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 - 5.0473964 - 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 - 6.0460704 - 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 - 7.0449922 - ÚI 2016 RIV SK cze C - Conference Paper (international conference)
Kůrková, Věra
Modelová složitost neuronových sítí - zdánlivý paradox.
[Model Complexity of Neural Networks - a Seeming Paradox.]
Kognícia a umelý život 2015. Bratislava: Univerzita Komenského v Bratislave, 2015 - (Farkaš, I.; Takáč, M.; Rybár, J.; Kelemen, J.), s. 102-106. ISBN 978-80-223-3875-2.
[Kognícia a umelý život /15./. Trenčianske Teplice (SK), 25.05.2015-28.05.2015]
R&D Projects: GA MŠMT(CZ) LD13002
Institutional support: RVO:67985807
Keywords : model complexity of feedforward neural networks * one-hidden-layer networks * concentration of measure
Subject RIV: IN - Informatics, Computer Science
http://cogsci.fmph.uniba.sk/kuz2015/zbornik/prispevky/kurkova.pdf
Permanent Link: http://hdl.handle.net/11104/0251322File Download Size Commentary Version Access a0449922.pdf 2 2.2 MB Publisher’s postprint require 0449922.pdf 1 762.7 KB Author´s preprint open-access - 8.0447921 - ÚI 2016 RIV DE eng C - Conference Paper (international conference)
Kůrková, Věra
Limitations of One-Hidden-Layer Perceptron Networks.
Proceedings ITAT 2015: Information Technologies - Applications and Theory. Aachen & Charleston: Technical University & CreateSpace Independent Publishing Platform, 2015 - (Yaghob, J.), s. 167-171. CEUR Workshop Proceedings, V-1422. ISBN 978-1-5151-2065-0. ISSN 1613-0073.
[ITAT 2015. Conference on Theory and Practice of Information Technologies /15./. Slovenský Raj (SK), 17.09.2015-21.09.2015]
R&D Projects: GA MŠMT(CZ) LD13002
Institutional support: RVO:67985807
Keywords : perceptron networks * model complexity * representations of finite mappings by neural networks
Subject RIV: IN - Informatics, Computer Science
Permanent Link: http://hdl.handle.net/11104/0249675File Download Size Commentary Version Access a0447921.pdf 0 606.5 KB Publisher’s postprint require - 9.0446410 - 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 - 10.0443724 - 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