Search results
- 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 - 2.0558233 - 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/11104/0331975File Download Size Commentary Version Access Vinue-visus-2022-Biomass-assessment-and-carbon-seque.pdf 8 12.3 MB Publisher’s postprint open-access - 3.0550881 - ÚTIA 2022 RIV CZ eng V - Research Report
Nugent, Sh. - Quinn, Anthony
Transferring Improved Local Kernel Design in Multi-Source Bayesian Transfer Learning, with an application in Air Pollution Monitoring in India.
Praha: ÚTIA AV ČR, v. v. i.,, 2021. 19 s. Research Report, 2392.
R&D Projects: GA ČR(CZ) GA18-15970S
Institutional support: RVO:67985556
Keywords : fully probabilistic methods * Bayesian Transfer Learning algorithm * Gaussian Process * Intrinsic Coregionalization Model * pollution modelling
OECD category: Applied mathematics
http://library.utia.cas.cz/separaty/2021/AS/quinn-0550881.pdf
Permanent Link: http://hdl.handle.net/11104/0326186File Download Size Commentary Version Access 0550881.pdf 0 483 KB Other open-access - 4.0533901 - ÚI 2021 RIV DE eng C - Conference Paper (international conference)
Dvořák, M. - Pitra, Zbyněk - Holeňa, Martin
Assessment of Surrogate Model Settings Using Landscape Analysis.
Proceedings of the 20th Conference Information Technologies - Applications and Theory. Aachen: Technical University & CreateSpace Independent Publishing, 2020 - (Holeňa, M.; Horváth, T.; Kelemenová, A.; Mráz, F.; Pardubská, D.; Plátek, M.; Sosík, P.), s. 81-89. CEUR Workshop Proceedings, 2718. ISSN 1613-0073.
[ITAT 2020: Information Technologies - Applications and Theory /20./. Oravská Lesná (SK), 18.09.2020-22.09.2020]
R&D Projects: GA ČR(CZ) GA18-18080S
Institutional support: RVO:67985807
Keywords : Black-box optimization * CMA-ES * Surrogate modelling * Gaussian process * Landscape analysis
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-2718/paper20.pdf
Permanent Link: http://hdl.handle.net/11104/0312131File Download Size Commentary Version Access 0533901-aw.pdf 4 740.4 KB CC BY 4.0 Publisher’s postprint open-access - 5.0517961 - 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 - 6.0509320 - ÚI 2020 RIV DE eng C - Conference Paper (international conference)
Pitra, Zbyněk - Bajer, Lukáš - Holeňa, Martin
Knowledge-based Selection of Gaussian Process Surrogates.
IAL ECML PKDD 2019: Workshop & Tutorial on Interactive Adaptive Learning. Proceedings. Aachen: Technical University & CreateSpace Independent Publishing Platform, 2019 - (Kottke, D.; Lemaire, D.; Calma, A.; Krempl, G.; Holzinger, A.), s. 48-63. CEUR Workshop Proceedings, 2444. ISSN 1613-0073.
[ECML PKDD 2019: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Würzburg (DE), 16.09.2019-20.09.2019]
R&D Projects: GA ČR GA17-01251S; GA ČR(CZ) GA18-18080S
Grant - others:ČVUT(CZ) SGS17/193/OHK4/3T/14; GA MŠk(CZ) LM2015042
Institutional support: RVO:67985807
Keywords : Benchmarking * Black-box optimization * Gaussian process * Landscape analysis
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-2444/ialatecml_paper4.pdf
Permanent Link: http://hdl.handle.net/11104/0300063File Download Size Commentary Version Access 0509320-a.pdf 6 1.9 MB Publisher’s postprint require - 7.0508171 - ÚI 2020 RIV US eng C - Conference Paper (international conference)
Pitra, Zbyněk - Repický, Jakub - Holeňa, Martin
Landscape analysis of gaussian process surrogates for the covariance matrix adaptation evolution strategy.
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference. New York: ACM, 2019 - (López-Ibáñez, M.), s. 691-699. ISBN 978-1-4503-6111-8.
[GECCO 2019: The Genetic and Evolutionary Computation Conference. Prague (CZ), 13.07.2019-17.07.2019]
R&D Projects: GA ČR GA17-01251S; GA ČR(CZ) GA18-18080S
Grant - others:GA MŠk(CZ) LM2015042
Institutional support: RVO:67985807
Keywords : black-box optimization * evolutionary optimization * surrogate modelling * Gaussian process * landscape analysis
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/11104/0299146 - 8.0506867 - ÚI 2020 RIV US eng A - Abstract
Bajer, Lukáš - Pitra, Zbyněk - Repický, Jakub - Holeňa, Martin
Gaussian Process Surrogate Models for the CMA-ES.
GECCO '19. Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York: ACM, 2019. s. 17-18. ISBN 978-1-4503-6748-6.
[GECCO 2019: The Genetic and Evolutionary Computation Conference. 13.07.2019-17.07.2019, Prague]
R&D Projects: GA ČR GA17-01251S; GA ČR(CZ) GA18-18080S
Institutional support: RVO:67985807
Keywords : black-box optimization * evolutionary optimization * surrogate modelling * Gaussian process
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/11104/0298001 - 9.0506861 - ÚTIA 2020 CZ eng J - Journal Article
Přikryl, Jan - Kocijan, J.
Modelling Occupancy-Queue Relation Using Gaussian Process.
Neural Network World. Roč. 25, č. 1 (2015), s. 35-52. ISSN 1210-0552
R&D Projects: GA MŠMT 1M0572; GA MŠMT(CZ) MEB091015
Institutional support: RVO:67985556
Keywords : queue estimation * uncertainty * traffic model * Gaussian process
OECD category: Statistics and probability
Impact factor: 0.562, year: 2015
http://library.utia.cas.cz/separaty/2019/AS/prikryl-0506861.pdf
Permanent Link: http://hdl.handle.net/11104/0298023 - 10.0493292 - ÚI 2019 RIV IE eng A - Abstract
Repický, Jakub - Pitra, Zbyněk - Holeňa, Martin
Adaptive Selection of Gaussian Process Model for Active Learning in Expensive Optimization.
ECML PKDD 2018: Workshop on Interactive Adaptive Learning. Proceedings. Dublin, 2018 - (Krempl, G.; Lemaire, V.; Kottke, D.; Calma, A.; Holzinger, A.; Polikar, R.; Sick, B.). s. 80-84
[ECML PKDD 2018: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 10.09.2018-14.09.2018, Dublin]
R&D Projects: GA ČR GA17-01251S
Institutional support: RVO:67985807
Keywords : Gaussian process * Surrogate model * Black-box optimization * Active Learning
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://www.ies.uni-kassel.de/p/ial2018/ialatecml2018.pdf
Permanent Link: http://hdl.handle.net/11104/0286678File Download Size Commentary Version Access a0493292.pdf 7 715 KB Sborník dostupný online. Publisher’s postprint open-access