State Sequence Modeling

State Sequence Modeling is a computational framework within sequence pattern recognition that characterizes temporal dynamics by representing system behaviors as transitions among discrete states, distinguished by its explicit modeling of state dependencies and emission processes in sequential data.

181

Publications

9.3K

Citations

534

Authors

239

Institutions

Publications per year

2017–2026

55

Authors

534

  1. MI
    Michael I. Jordan

    University of California, Berkeley, United States

    5 publications · 455 citations

    5

    h-index

  2. TK
    Tadashi Kitamura

    Kitasato University, Japan

    5 publications · 1.4K citations

    5

    h-index

  3. KT
    Keiichi Tokuda

    Nagoya Institute of Technology, Japan

    5 publications · 1.4K citations

    5

    h-index

  4. EB
    Emily B. Fox

    University of Washington, United States

    4 publications · 370 citations

    4

    h-index

  5. AS
    Alan S. Willsky

    Massachusetts Institute of Technology, United States

    4 publications · 370 citations

    4

    h-index

1–5 of 534

Venues

  1. arXiv (Cornell University)

    Repository · 8 publications · 230 citations

    8

    h-index

  2. IEEE Access

    4 publications · 128 citations

    4

    h-index

  3. IEEE Transactions on Signal Processing

    4 publications · 119 citations

    4

    h-index

  4. Computer Speech & Language

    3 publications · 203 citations

    3

    h-index

  5. IEEE Transactions on Information Theory

    3 publications · 86 citations

    3

    h-index

1–5