Search results

  1. 1.
    0559729 - ÚTIA 2023 RIV NL eng J - Journal Article
    Papež, Milan - Quinn, Anthony
    Transferring model structure in Bayesian transfer learning for Gaussian process regression.
    Knowledge-Based System. Roč. 251, č. 1 (2022), č. článku 108875. ISSN 0950-7051. E-ISSN 1872-7409
    R&D Projects: GA ČR(CZ) GA18-15970S
    Institutional support: RVO:67985556
    Keywords : Bayesian transfer learning (BTL) * Multitask learning * Local and global modelling * Fully probabilistic design * Incomplete modelling * Gaussian process regression
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    Impact factor: 8.8, year: 2022
    Method of publishing: Limited access
    http://library.utia.cas.cz/separaty/2022/AS/papez-0559729.pdf https://www.sciencedirect.com/science/article/pii/S095070512200418X?via%3Dihub
    Permanent Link: https://hdl.handle.net/11104/0333424
     
     
  2. 2.
    0558233 - ÚVGZ 2023 RIV CH eng J - Journal Article
    Vinue-Visus, D. - Ruiz-Peinado, R. - Fuente Herraiz, David - Oliver-Villanueva, J. V. - Coll-Aliaga, E. - Lerma-Arce, V.
    Biomass Assessment and Carbon Sequestration in Post-Fire Shrublands by Means of Sentinel-2 and Gaussian Processes.
    Forests. Roč. 13, č. 5 (2022), č. článku 771. E-ISSN 1999-4907
    Institutional support: RVO:86652079
    Keywords : forest * retrieval * boreal * machine learning * remote sensing * Gaussian process regression * forest inventory
    OECD category: Forestry
    Impact factor: 2.9, year: 2022
    Method of publishing: Open access
    https://www.mdpi.com/1999-4907/13/5/771
    Permanent Link: http://hdl.handle.net/11104/0331975
    FileDownloadSizeCommentaryVersionAccess
    Vinue-visus-2022-Biomass-assessment-and-carbon-seque.pdf812.3 MBPublisher’s postprintopen-access
     
     
  3. 3.
    0517961 - ÚTIA 2021 RIV US eng C - Conference Paper (international conference)
    Papež, Milan - Quinn, Anthony
    Bayesian transfer learning between Gaussian process regression tasks.
    Proceedings of the IEEE International Symposium on Signal Processing and Information Technology 2019 (ISSPIT 2019). Piscataway: IEEE, 2019. ISBN 978-1-7281-5341-4.
    [IEEE International Symposium on Signal Processing and Information Technology 2019 (ISSPIT 2019) /19./. Ajman (AE), 09.12.2019-12.12.2019]
    R&D Projects: GA ČR(CZ) GA18-15970S
    Institutional support: RVO:67985556
    Keywords : Bayesian transfer learning * supervised learning * fully probabilistic design * incomplete modelling * Gaussian process regression
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    http://library.utia.cas.cz/separaty/2019/AS/papez-0517961.pdf
    Permanent Link: http://hdl.handle.net/11104/0303180
     
     
  4. 4.
    0477789 - ÚI 2018 RIV US eng C - Conference Paper (international conference)
    Pitra, Z. - Bajer, L. - Repický, J. - Holeňa, Martin
    Comparison of Ordinal and Metric Gaussian Process Regression as Surrogate Models for CMA Evolution Strategy.
    GECCO 2017. Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York: ACM, 2017, s. 1764-1771. ISBN 978-1-4503-4939-0.
    [GECCO 2017. Genetic and Evolutionary Computation Conference. Berlin (DE), 15.07.2017-19.07.2017]
    R&D Projects: GA ČR GA17-01251S
    Grant - others:GA MŠk(CZ) LO1611; ČVUT(CZ) SGS17/193/OHK4/3T/14; GA MŠk(CZ) LM2010005
    Institutional support: RVO:67985807
    Keywords : black-box optimization * evolutionary optimization * surrogate modelling * Gaussian-process regression
    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/11104/0274013
    FileDownloadSizeCommentaryVersionAccess
    a0477789.pdf11.2 MBPublisher’s postprintrequire
     
     
  5. 5.
    0477787 - ÚI 2018 US eng A - Abstract
    Pitra, Z. - Bajer, L. - Repický, J. - Holeňa, Martin
    Ordinal versus metric gaussian process regression in surrogate modelling for CMA evolution strategy.
    GECCO 2017. Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York: ACM, 2017. s. 177-178. ISBN 978-1-4503-4939-0.
    [GECCO 2017. Genetic and Evolutionary Computation Conference. 15.07.2017-19.07.2017, Berlin]
    R&D Projects: GA ČR GA17-01251S
    Grant - others:GA MŠk(CZ) LO1611; ČVUT(CZ) SGS17/193/OHK4/3T/14
    Institutional support: RVO:67985807
    Keywords : black-box optimization * evolutionary optimization * surrogate modelling * Gaussian-process regression
    Subject RIV: IN - Informatics, Computer Science
    Permanent Link: http://hdl.handle.net/11104/0274011
    FileDownloadSizeCommentaryVersionAccess
    a0477787.pdf2668.9 KBPublisher’s postprintrequire
     
     


  This site uses cookies to make them easier to browse. Learn more about how we use cookies.