Multivariate Representation Learning

Multivariate Representation Learning is a machine learning framework that learns latent feature embeddings from multivariate time series data by jointly modeling the interdependencies among multiple correlated variables over time to improve classification performance.

115

Publications

6.8K

Citations

388

Authors

196

Institutions

Publications per year

2017–2026

63

Authors

388

  1. RJ
    Robert Jenssen

    UiT The Arctic University of Norway, Norway

    4 publications · 387 citations

    4

    h-index

  2. YL
    Yan Liu

    Hong Kong Polytechnic University, Hong Kong SAR China

    4 publications · 131 citations

    3

    h-index

  3. FM
    Filippo Maria Bianchi

    UiT The Arctic University of Norway, Norway

    3 publications · 285 citations

    3

    h-index

  4. KØ
    Karl Øyvind Mikalsen

    UiT The Arctic University of Norway, Norway

    2 publications · 164 citations

    2

    h-index

  5. QB
    Quentin Barthélemy

    GIPSA-Lab, France

    2 publications · 84 citations

    2

    h-index

1–5 of 388

Venues

  1. Pattern Recognition

    7 publications · 338 citations

    7

    h-index

  2. arXiv (Cornell University)

    Repository · 5 publications · 251 citations

    5

    h-index

  3. Expert Systems with Applications

    5 publications · 110 citations

    5

    h-index

  4. Neurocomputing

    4 publications · 381 citations

    4

    h-index

  5. Pattern Recognition Letters

    3 publications · 619 citations

    3

    h-index

1–5