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
Parents
167
A Benchmark for Data Imputation Methods
Sebastian Jäger, Arndt Allhorn, Felix Bießmann · Frontiers in Big Data · 2021 · 219 citations · Full text
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Hong Kong University of Science and Technology, Hong Kong SAR China
3 publications · 76 citations
3
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3
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348

Chinese Academy of Sciences
Beijing, China
7 h-index
12 publications
1.7K citations
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
5 h-index
17 publications
516 citations
University of Cambridge
Cambridge, United Kingdom
5 h-index
10 publications
352 citations

Centre National de la Recherche Scientifique
Paris, France
5 h-index
15 publications
1.1K citations
Yonsei University
Seoul, South Korea
5 h-index
8 publications
192 citations
Stanford University
Stanford, United States
4 h-index
9 publications
599 citations
University of Chicago
Chicago, United States
4 h-index
4 publications
155 citations

Harbin Institute of Technology
Harbin, China
4 h-index
11 publications
206 citations
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Repository · 12 publications · 3.4K citations
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5 publications · 765 citations
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5 publications · 182 citations
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4 publications · 254 citations
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3 publications · 537 citations
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