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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

  • 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