Variational Feature Modeling

Variational Feature Modeling is a computational modeling paradigm within feature-based modeling that formally integrates probabilistic variations and uncertainties into the representation and manipulation of features, distinguishing it by its foundation in variational inference principles to enable robust, adaptive, and statistically grounded feature configurations.

15

Publications

535

Citations

59

Authors

26

Institutions

Publications per year

2017–2026

11

Authors

59

  1. WH
    Wenkai Hu

    China University of Geosciences, China

    1 publications · 42 citations

    1

    h-index

  2. JD
    Jian Ding

    Wuhan University, China

    1 publications · 126 citations

    1

    h-index

  3. SG
    S. Ghebreab

    University of Amsterdam, Netherlands

    1 publications · 10 citations

    1

    h-index

  4. ZC
    Zhengrong Cheng

    University of Alberta, Canada

    1 publications · 50 citations

    1

    h-index

  5. HL
    Hieu Lê

    Stony Brook University, United States

    1 publications · 71 citations

    1

    h-index

1–5 of 59

Venues

  1. Neurocomputing

    2 publications · 85 citations

    2

    h-index

  2. 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

    Conference · 1 publications · 71 citations

    1

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 126 citations

    1

    h-index

  4. 1

    h-index

  5. 1

    h-index

1–5