Search results

  1. 1.
    0587051 - ÚI 2025 eng C - Conference Paper (international conference)
    Kadlecová, Gabriela - Lukasik, J. - Pilát, Martin - Vidnerová, Petra - Safari, M. - Neruda, Roman - Hutter, F.
    Surprisingly Strong Performance Prediction with Neural Graph Features.
    [ICML 2024: International Conference on Machine Learning /41./. Vienna (AT), 21.07.2024-27.07.2024]
    R&D Projects: GA ČR(CZ) GA22-02067S
    Research Infrastructure: e-INFRA CZ II - 90254
    Institutional support: RVO:67985807
    Keywords : Neural architecture search * Zero-cost proxies * Performance prediction
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    Permanent Link: https://hdl.handle.net/11104/0354383
    FileDownloadSizeCommentaryVersionAccess
    0587051-fin.pdf61.7 MBCC BY 4.0Author´s preprintopen-access
     
     
  2. 2.
    0561586 - ÚI 2023 RIV US eng C - Conference Paper (international conference)
    Pilát, M. - Suchopárová, Gabriela
    Using graph neural networks as surrogate models in genetic programming.
    GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference. New York: ACM, 2022 - (Fieldsend, J.), s. 582-585. ISBN 978-1-4503-9268-6.
    [GECCO 2022: Genetic and Evolutionary Computation Conference. Boston (US), 09.07.2022-13.07.2022]
    Grant - others:Ministerstvo školství, mládeže a tělovýchovy - GA MŠk(CZ) LM2018140
    Institutional support: RVO:67985807
    Keywords : graph neural networks * genetic programming * surrogate models
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    https://dx.doi.org/10.1145/3520304.3529024
    Permanent Link: https://hdl.handle.net/11104/0334164
     
     
  3. 3.
    0560713 - ÚI 2023 RIV US eng C - Conference Paper (international conference)
    Suchopárová, Gabriela - Neruda, Roman
    Graph Embedding for Neural Architecture Search with Input-Output Information.
    Auto-ML Conf 2022: Accepted Papers: Late-Breaking Workshop. Baltimore: AutoML Conference, 2022.
    [Auto-ML 2022: International Conference on Automated Machine Learning /1./. Baltimore (US), 25.07.2022-27.07.2022]
    Grant - others:Ministerstvo školství, mládeže a tělovýchovy - GA MŠk(CZ) LM2018140
    Institutional support: RVO:67985807
    Keywords : machine learning * neural architecture search * meta-learning * graph neural networks * representation learning
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    Permanent Link: https://hdl.handle.net/11104/0333566
    FileDownloadSizeCommentaryVersionAccess
    0560713-aoa.pdf4673.3 KBOA CC BY 4.0 (v clanku)Publisher’s postprintopen-access
     
     
  4. 4.
    0558938 - ÚI 2023 RIV CH eng C - Conference Paper (international conference)
    Suchopárová, Gabriela - Vidnerová, Petra - Neruda, Roman - Šmíd, Martin
    Using a Deep Neural Network in a Relative Risk Model to Estimate Vaccination Protection for COVID-19.
    Engineering Applications of Neural Networks. Cham: Springer, 2022 - (Iliadis, L.; Jayne, C.; Tefas, A.; Pimenidis, E.), s. 310-320. Communications in Computer and Information Science, 1600. ISBN 978-3-031-08222-1. ISSN 1865-0929.
    [EANN 2022: International Conference on Engineering Applications of Neural Networks /23./. Chersonissos / Virtual (GR), 17.06.2022-20.06.2022]
    Institutional support: RVO:67985807 ; RVO:67985556
    Keywords : Deep learning * Risk model * Immunity waning
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8); Statistics and probability (UTIA-B)
    https://dx.doi.org/10.1007/978-3-031-08223-8_26
    Permanent Link: https://hdl.handle.net/11104/0332424
     
     
  5. 5.
    0546282 - ÚI 2022 RIV DE eng C - Conference Paper (international conference)
    Vidnerová, Petra - Neruda, Roman - Suchopárová, Gabriela - Berec, L. - Diviák, T. - Kuběna, Aleš Antonín - Levínský, René - Šlerka, J. - Šmíd, Martin - Trnka, J. - Tuček, V. - Vrbenský, Karel - Zajíček, Milan … Total 14 authors
    Simulation of non-pharmaceutical interventions in an agent based epidemic model.
    Proceedings of the 21st Conference Information Technologies – Applications and Theory (ITAT 2021). Aachen: Technical University & CreateSpace Independent Publishing, 2021 - (Brejová, B.; Ciencialová, L.; Holeňa, M.; Mráz, F.; Pardubská, D.; Plátek, M.; Vinař, T.), s. 263-268. ISSN 1613-0073.
    [ITAT 2021: Information Technologies - Applications and Theory /21./. Heľpa (SK), 24.09.2021-28.09.2021]
    R&D Projects: GA TA ČR(CZ) TL04000282
    Institutional support: RVO:67985807 ; RVO:67985556 ; RVO:67985998
    Keywords : agent based modelling * epidemic modelling * non-pharmaceutical interventions
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8); Sociology (NHU-N); Public administration (NHU-N); Urban studies (planning and development) (NHU-N); Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) (UTIA-B)
    https://ics.upjs.sk/~antoni/ceur-ws.org/Vol-0000/paper12.pdf
    Permanent Link: http://hdl.handle.net/11104/0322820
    FileDownloadSizeCommentaryVersionAccess
    0546282-aoa.pdf12924.6 KBOA CC BY 4.0Publisher’s postprintopen-access
     
     
  6. 6.
    0537567 - ÚI 2021 RIV US eng C - Conference Paper (international conference)
    Suchopárová, Gabriela - Neruda, Roman
    Genens: An AutoML System for Ensemble Optimization Based on Developmental Genetic Programming.
    2020 IEEE Symposium Series on Computational Intelligence (SSCI). New York: IEEE, 2020, s. 631-638. ISBN 978-1-7281-2547-3.
    [IEEE SSCI 2020: IEEE Symposium Series on Computational Intelligence. Canberra / Online (AU), 01.12.2020-04.12.2020]
    R&D Projects: GA ČR(CZ) GA18-23827S
    Institutional support: RVO:67985807
    Keywords : Machine learning * AutoML * Genetic programming * Developmental methods * Pipelines * Vegetation * Optimization * Task analysis * Machine learning algorithms * Computational modeling * Benchmark testing
    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/11104/0315396
     
     


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