SISAP 2023 Indexing Challenge – Learned Metric Index
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00132045" target="_blank" >RIV/00216224:14330/23:00132045 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-031-46994-7_24" target="_blank" >http://dx.doi.org/10.1007/978-3-031-46994-7_24</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-46994-7_24" target="_blank" >10.1007/978-3-031-46994-7_24</a>
Alternative languages
Result language
angličtina
Original language name
SISAP 2023 Indexing Challenge – Learned Metric Index
Original language 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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Similarity Search and Applications. SISAP 2023. Lecture Notes in Computer Science, vol 14289
ISBN
9783031469930
ISSN
0302-9743
e-ISSN
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Number of pages
9
Pages from-to
282-290
Publisher name
Springer
Place of publication
Cham
Event location
Cham
Event date
Jan 1, 2023
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
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