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@inproceedings{2330525,
author = {Slanináková, Terézia and Procházka, David and Antol, Matej and Oľha, Jaroslav and Dohnal, Vlastislav},
address = {Cham},
booktitle = {Similarity Search and Applications. SISAP 2023. Lecture Notes in Computer Science, vol 14289},
doi = {http://dx.doi.org/10.1007/978-3-031-46994-7_24},
editor = {Pedreira, O., Estivill-Castro, V.},
keywords = {sisap indexing challenge; learned metric index; similarity search; machine learning for indexing; performance benchmarking},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Cham},
isbn = {978-3-031-46993-0},
pages = {282-290},
publisher = {Springer},
title = {SISAP 2023 Indexing Challenge – Learned Metric Index},
year = {2023}
}
TY - JOUR
ID - 2330525
AU - Slanináková, Terézia - Procházka, David - Antol, Matej - Oľha, Jaroslav - Dohnal, Vlastislav
PY - 2023
TI - SISAP 2023 Indexing Challenge – Learned Metric Index
PB - Springer
CY - Cham
SN - 9783031469930
KW - sisap indexing challenge
KW - learned metric index
KW - similarity search
KW - machine learning for indexing
KW - performance benchmarking
N2 - 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.
ER -
SLANINÁKOVÁ, Terézia, David PROCHÁZKA, Matej ANTOL, Jaroslav OĽHA and Vlastislav DOHNAL. SISAP 2023 Indexing Challenge – Learned Metric Index. In Pedreira, O., Estivill-Castro, V. \textit{Similarity Search and Applications. SISAP 2023. Lecture Notes in Computer Science, vol 14289}. Cham: Springer, 2023, p.~282-290. ISBN~978-3-031-46993-0. Available from: https://dx.doi.org/10.1007/978-3-031-46994-7\_{}24.
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