Search results
- 1.0585922 - ÚI 2025 C - Conference Paper (international conference)
Dědič, M. - Bajer, L. - Procházka, P. - Holeňa, Martin
Balancing performance and complexity with adaptive graph coarsening.
The Second Tiny Papers Track at ICLR 2024. OpenReview.net / ICLR, 2024.
[ICLR 2024. International Conference on Learning Representations /12./. Vienna (AT), 07.05.2024-11.05.2024]
Institutional support: RVO:67985807
Keywords : Graph representation learning * Graph coarsening * Performance-complexity trade-off * HARP
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://openreview.net/forum?id=DrHwIzz93C
Permanent Link: https://hdl.handle.net/11104/0353559File Download Size Commentary Version Access 0585922-oaf.pdf 0 249.5 KB OA CC BY 4.0 Publisher’s postprint open-access - 2.0580726 - ÚI 2024 RIV PT eng C - Conference Paper (international conference)
Korel, L. - Behr, A. S. - Kockmann, N. - Holeňa, Martin
Using Paraphrasers to Detect Duplicities in Ontologies.
Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2. Setubal: SciTePress, 2023 - (Aveiro, D.; Dietz, J.; Poggi, A.; Bernardino, J.), s. 40-49. ISBN 978-989-758-671-2. ISSN 2184-3228.
[KEOD 2023: Conference on Knowledge Engineering and Ontology Development /15./. Rome / hybrid (IT), 13.11.2023-15.11.2023]
Research Infrastructure: ELIXIR CZ III - 90255; e-INFRA CZ II - 90254
Institutional support: RVO:67985807
Keywords : Classifiers * Duplicity Detection * Ontologies * Paraphrasers * Representation Learning * Semantic Similarity
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://doi.org/10.5220/0012164500003598
Permanent Link: https://hdl.handle.net/11104/0349480File Download Size Commentary Version Access 0580726-aoa.pdf 1 412.7 KB OA CC BY-NC-ND 4.0 Publisher’s postprint open-access - 3.0568306 - ÚI 2024 RIV CH eng J - Journal Article
Korel, L. - Yorsh, U. - Behr, A. S. - Kockmann, N. - Holeňa, Martin
Text-to-Ontology Mapping via Natural Language Processing with Application to Search for Relevant Ontologies in Catalysis.
Computers. Roč. 12, č. 1 (2023), č. článku 14. ISSN 2073-431X
Institutional support: RVO:67985807
Keywords : text representation learning * text classification * text preprocessing * text data * ontology
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 2.8, year: 2022
Method of publishing: Open access
https://dx.doi.org/10.3390/computers12010014
Permanent Link: https://hdl.handle.net/11104/0339633File Download Size Commentary Version Access 0568306-afinoa.pdf 3 1.9 MB OA CC BY 4.0 Publisher’s postprint open-access - 4.0565961 - ÚI 2023 RIV DE eng C - Conference Paper (international conference)
Korel, L. - Behr, A. S. - Kockmann, N. - Holeňa, Martin
Using Artificial Neural Networks to Determine Ontologies Most Relevant to Scientific Texts.
Proceedings of the 22st Conference Information Technologies – Applications and Theory (ITAT 2022). Aachen: Technical University & CreateSpace Independent Publishing, 2022 - (Ciencialová, L.; Holeňa, M.; Jajcay, R.; Jajcayová, R.; Mráz, F.; Pardubská, D.; Plátek, M.), s. 44-54. ISSN 1613-0073.
[ITAT 2022: Conference Information Technologies - Applications and Theory /22./. Zuberec (SK), 23.09.2022-27.09.2022]
Institutional support: RVO:67985807
Keywords : ontology * text data * text preprocessing * text representation learning * text classification
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://ceur-ws.org/Vol-3226/paper5.pdf
Permanent Link: https://hdl.handle.net/11104/0337426 - 5.0560713 - 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/11104/0333566File Download Size Commentary Version Access 0560713-aoa.pdf 4 673.3 KB OA CC BY 4.0 (v clanku) Publisher’s postprint open-access - 6.0546251 - ÚI 2022 RIV DE eng C - Conference Paper (international conference)
Borisov, S. - Dědič, M. - Holeňa, Martin
Experimental Investigation of Neural and Weisfeiler-Lehman-Kernel Graph Representations for Downstream SVM-Based Classification.
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. 130-139. ISSN 1613-0073.
[ITAT 2021: Information Technologies - Applications and Theory /21./. Heľpa (SK), 24.09.2021-28.09.2021]
R&D Projects: GA ČR(CZ) GA18-18080S
Institutional support: RVO:67985807
Keywords : graph representation learning * graph neural networks * message-passing networks * Weisfeiler-Lehman isomorphism test * Weisfeiler-Lehman subtree kernel
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://ics.upjs.sk/~antoni/ceur-ws.org/Vol-0000/paper50.pdf
Permanent Link: http://hdl.handle.net/11104/0322814File Download Size Commentary Version Access 0546251-aoa.pdf 2 202.6 KB OA CC BY 4.0 Publisher’s postprint open-access - 7.0533916 - ÚI 2021 RIV DE eng C - Conference Paper (international conference)
Dědič, M. - Pevný, T. - Bajer, L. - Holeňa, Martin
Loss Functions for Clustering in Multi-instance Learning.
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. 137-146. 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
Grant - others:GA ČR(CZ) GA18-21409S
Program: GA
Institutional support: RVO:67985807
Keywords : Representation learning * Multi-instance learning * Multi-instance clustering * Clustering loss functions * Intrusion detection
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/paper05.pdf
Permanent Link: http://hdl.handle.net/11104/0312145File Download Size Commentary Version Access 0533916-aw.pdf 2 595.1 KB CC BY 4.0 Publisher’s postprint open-access