SISAP 2023 Indexing Challenge – Learned Metric Index
Authors: 
Terèzia Slanináková, 
David Procházka, Matej Antol, Jaroslav Olha, Vlastislav DohnalAuthors Info & Claims
Terèzia Slanináková, David Procházka, Matej Antol, Jaroslav Olha, Vlastislav DohnalAuthors Info & ClaimsPages 282 - 290
Abstract
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.
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Oct 2023
324 pages
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
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Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 27 October 2023
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