Sequence Embedding

Sequence Embedding is a representational method that maps sequential data into a structured, lower-dimensional space while preserving inherent temporal or ordinal relationships among elements in the sequence.

69

Publications

4.9K

Citations

221

Authors

94

Institutions

Publications per year

2017–2026

49

Authors

221

  1. EA
    Ehsaneddin Asgari

    University of California, Berkeley, United States

    3 publications · 128 citations

    3

    h-index

  2. AC
    Alice C. McHardy

    Helmholtz Centre for Infection Research, Germany

    3 publications · 128 citations

    3

    h-index

  3. MR
    Mohammad R. K. Mofrad

    University of California, Berkeley, United States

    3 publications · 128 citations

    3

    h-index

  4. JE
    Jesse Engel

    Google (United States), United States

    2 publications · 325 citations

    2

    h-index

  5. DE
    Douglas Eck

    Université de Montréal, Canada

    2 publications · 325 citations

    2

    h-index

1–5 of 221

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 397 citations

    5

    h-index

  2. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 3 publications · 58 citations

    4

    h-index

  3. Bioinformatics

    3 publications · 269 citations

    3

    h-index

  4. International Conference on Machine Learning

    Conference · 2 publications · 100 citations

    2

    h-index

  5. 1

    h-index

1–5