Deep Learning Imputation

Deep Learning Imputation is a methodological approach within missing data imputation that employs deep neural networks to model complex, nonlinear relationships in data for the purpose of accurately estimating missing values.

167

Publications

11.3K

Citations

681

Authors

348

Institutions

Publications per year

2017–2026

155

Authors

681

  1. GJ
    G. J. Conduit

    University of Cambridge, United Kingdom

    4 publications · 184 citations

    4

    h-index

  2. TM
    Thomas M. Whitehead

    University of Cambridge, United Kingdom

    3 publications · 111 citations

    3

    h-index

  3. MS
    Matthew Segall

    Optibrium (United Kingdom), United Kingdom

    3 publications · 111 citations

    3

    h-index

  4. WW
    Wenjia Wang

    Hong Kong University of Science and Technology, Hong Kong SAR China

    3 publications · 76 citations

    3

    h-index

  5. BI
    Benedict Irwin

    University of Cambridge, United Kingdom

    3 publications · 111 citations

    3

    h-index

1–5 of 681

Venues

  1. arXiv (Cornell University)

    Repository · 12 publications · 3.4K citations

    12

    h-index

  2. 5

    h-index

  3. IEEE Access

    5 publications · 182 citations

    5

    h-index

  4. IEEE Internet of Things Journal

    4 publications · 254 citations

    4

    h-index

  5. Journal of Hydrology

    3 publications · 537 citations

    3

    h-index

1–5