Conditional Random Fields

Conditional Random Fields is a discriminative probabilistic model for structured prediction that specifies a conditional probability distribution over label sequences given an observation sequence, distinguished from generative models by directly modeling the conditional dependence of labels on observations without assuming independence among inputs.

2.5K

Publications

224.5K

Citations

6.9K

Authors

1.6K

Institutions

Publications per year

2017–2026

954

Authors

6.9K

  1. AM
    Andrew McCallum

    University of Massachusetts Amherst, United States

    37 publications · 19.4K citations

    31

    h-index

  2. XW
    Xiaolong Wang

    Harbin Institute of Technology, China

    20 publications · 1.1K citations

    18

    h-index

  3. HX
    Hua Xu

    The University of Texas Health Science Center at Houston, United States

    16 publications · 1.2K citations

    16

    h-index

  4. AY
    Alan Yuille

    Johns Hopkins University, United States

    15 publications · 28.7K citations

    14

    h-index

  5. BT
    Buzhou Tang

    Harbin Institute of Technology, China

    15 publications · 1K citations

    14

    h-index

1–5 of 6.9K

Venues

  1. arXiv (Cornell University)

    Repository · 113 publications · 16.5K citations

    38

    h-index

  2. IEEE Access

    56 publications · 2.6K citations

    29

    h-index

  3. 28

    h-index

  4. 26

    h-index

  5. Bioinformatics

    33 publications · 2.7K citations

    24

    h-index

1–5