SISAP 2023 Indexing Challenge – Learned Metric Index

Investor logo

Warning

This publication doesn't include Faculty of Arts. It includes Faculty of Informatics. Official publication website can be found on muni.cz.
Authors

SLANINÁKOVÁ Terézia PROCHÁZKA David ANTOL Matej OĽHA Jaroslav DOHNAL Vlastislav

Year of publication 2023
Type Article in Proceedings
Conference Similarity Search and Applications. SISAP 2023. Lecture Notes in Computer Science, vol 14289
MU Faculty or unit

Faculty of Informatics

Citation
Doi http://dx.doi.org/10.1007/978-3-031-46994-7_24
Keywords sisap indexing challenge; learned metric index; similarity search; machine learning for indexing; performance benchmarking
Description This submission into the SISAP Indexing Challenge examines the experimental setup and performance of the Learned Metric Index, which uses an architecture of interconnected learned models to answer similarity queries. An inherent part of this design is a great deal of flexibility in the implementation, such as the choice of particular machine learning models, or their arrangement in the overall architecture of the index. Therefore, for the sake of transparency and reproducibility, this report thoroughly describes the details of the specific Learned Metric Index implementation used to tackle the challenge.
Related projects:

You are running an old browser version. We recommend updating your browser to its latest version.