Start small, think big: On hyperparameter optimization for large-scale knowledge graph embeddings

DOI:	https://doi.org/10.1007/978-3-031-26390-3_9
URL:	https://link.springer.com/chapter/10.1007/978-3-03...
Additional URL:	https://www.researchgate.net/publication/369310138...
Document Type:	Conference or workshop publication
Year of publication:	2023
Book title:	Machine learning and knowledge discovery in databases : European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022 : proceedings. Part II
The title of a journal, publication series:	Lecture Notes in Computer Science
Volume:	13714
Page range:	138-154
Conference title:	ECML PKDD 2022
Location of the conference venue:	Grenoble, France
Date of the conference:	19.-23.09.2022
Publisher:	Amini, Massih-Reza ; Canu, Stéphane ; Fischer, Asja ; et al.
Place of publication:	Berlin [u.a.]
Publishing house:	Springer
ISBN:	978-3-031-26389-7 , 978-3-031-26390-3
ISSN:	0302-9743 , 1611-3349
Publication language:	English
Institution:	School of Business Informatics and Mathematics > Practical Computer Science I: Data Analytics (Gemulla 2014-)
Subject:	004 Computer science, internet
Keywords (English):	knowledge graph embedding , multi-fidelity hyperparameter optimization , low-fidelity approximation

Dieser Eintrag ist Teil der Universitätsbibliographie.

BASE: Kochsiek, Adrian ; Niesel, Fritz ; Gemulla, Rainer

Google Scholar: Kochsiek, Adrian ; Niesel, Fritz ; Gemulla, Rainer

ORCID: Kochsiek, Adrian ; Niesel, Fritz ; Gemulla, Rainer

Hits per month over past year

You have found an error? Please let us know about your desired correction here: E-Mail

Show item