Latent Feature Models

Latent Feature Models is a type of latent variable model that represents observed data as generated from a set of underlying, non-observable features, where each feature can be actively present or absent in individual data instances, thereby allowing overlapping feature memberships and capturing complex data structures through probabilistic assignments.

431

Publications

30.4K

Citations

1.5K

Authors

489

Institutions

Publications per year

2017–2026

287

Authors

1.5K

  1. ZG
    Zoubin Ghahramani

    University of Cambridge, United Kingdom

    10 publications · 1.2K citations

    10

    h-index

  2. TL
    Thomas L. Griffiths

    University of California, Berkeley, United States

    5 publications · 1.3K citations

    5

    h-index

  3. XL
    Xin Luo

    Chongqing Institute of Green and Intelligent Technology, China

    5 publications · 297 citations

    5

    h-index

  4. KT
    Kurt T. Miller

    University of California, Berkeley, United States

    5 publications · 636 citations

    5

    h-index

  5. LC
    Lawrence Carin

    Duke University, United States

    5 publications · 408 citations

    5

    h-index

1–5 of 1.5K

Venues

  1. arXiv (Cornell University)

    Repository · 40 publications · 1.6K citations

    22

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 11 publications · 431 citations

    11

    h-index

  3. 11

    h-index

  4. 9

    h-index

  5. IEEE Transactions on Image Processing

    8 publications · 1K citations

    8

    h-index

1–5