Variational Autoencoder Imputation
Variational Autoencoder Imputation is a missing data imputation method that leverages a probabilistic autoencoding framework to model the data distribution and generate plausible values for missing entries through variational inference.
26
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1.5K
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103
Authors
56
Institutions
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2017–2026
25
Variants
Vae Imputation
Parents
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Guillem Boquet, Antoni Morell, Javier Serrano et al. · Transportation Research Part C Emerging Technologies · 2020 · 151 citations · Full text
Genomic data imputation with variational auto-encoders
Yeping Lina Qiu, Hong Zheng, Olivier Gevaert · GigaScience · 2020 · 100 citations · Full text
Deep generative neural network for accurate drug response imputation
Peilin Jia, Ruifeng Hu, Guangsheng Pei et al. · Nature Communications · 2021 · 90 citations · Full text
Medicine, Bioinformatics, Oncology +17
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Universitat Autònoma de Barcelona
Cerdanyola del Vallès, Spain
2 h-index
8 publications
852 citations

University of Coimbra
Coimbra, Portugal
2 h-index
5 publications
348 citations
University of Cambridge
Cambridge, United Kingdom
2 h-index
7 publications
182 citations
Google (United States)
Mountain View, United States
2 h-index
2 publications
61 citations

Universidade do Porto
Porto, Portugal
2 h-index
2 publications
130 citations

Harbin Engineering University
Harbin, China
1 h-index
1 publications
18 citations
The University of Texas Health Science Center at Houston
Houston, United States
1 h-index
6 publications
540 citations
Kyung Hee University
Seoul, South Korea
1 h-index
4 publications
244 citations
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2 publications · 139 citations
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1 publications · 42 citations
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1 publications · 24 citations
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TOG · 1 publications · 69 citations
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Repository · 1 publications · 67 citations
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