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Universal representation learning for multivariate time series using the instance-level and cluster-level supervised contrastive learning

Data Mining and Knowledge Discovery(2024)10.1007/s10618-024-01006-1Source: DataRank Database

Universal representation learning for multivariate time series using the instance-level and cluster-level supervised contrastive learning is a research paper published in Data Mining and Knowledge Discovery (2024). On theSindex it has a DataRank of 0. It has been cited 8 times.

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Open Access8 citations

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Enrichment:Funding (6 grants)OA: hybridTopics (14)IDs (PubMed, PMC)

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Authors (8)

Suchetha Sharma,Ronen Bar‐Yoseph ORCID,Shlomit Radom‐Aizik ORCID,Kenneth C. Bilchick ORCID,Dan M. Cooper ORCID

Live enrichment

PubMed:39949582 PMC:PMC11825059

National Center for Advancing Translational Sciences

Grant: UL1TR003015/ KL2TR003016

NHLBI NIH HHS

Grant: R01 HL159945

NCATS NIH HHS

Grant: UL1 TR003015

NCATS NIH HHS

Grant: KL2 TR003016

National Institutes of Health

Grant: 1KL2TR003016-01

Institutional Career Development Core

National Institutes of Health

Grant: 3UL1TR003015-03S1

The integrated Translational Health Research Institute of Virginia (iTHRIV): Using Data to Improve Health

hybridLicense: CC BY

publisherhttps://link.springer.com/content/pdf/10.1007/s10618-024-01006-1.pdf

publisherhttps://link.springer.com/article/10.1007/s10618-024-01006-1/fulltext.html

Europe_PMChttps://europepmc.org/articles/PMC11825059

Europe_PMChttps://europepmc.org/articles/PMC11825059?pdf=render

https://doi.org/10.1007/s10618-024-01006-1

Fields of Study

02 engineering and technology0202 electrical engineering, electronic engineering, information engineering

Keywords

ClassificationInterpretabilityContrastive LearningMultivariate Time Series DataArtificial Intelligence and Image ProcessingData FormatArticleBrain DisordersMachine LearningInformation and Computing SciencesArtificial Intelligence & Image ProcessingInformation Systems