Autoencoder-based Imputation
Autoencoder-Based Imputation is a missing data imputation method that leverages an autoencoder neural network architecture to learn low-dimensional representations of complete data and reconstruct missing values through encoding and decoding processes.
73
Publications
4.2K
Citations
310
Authors
157
Institutions
Publications per year
2017–2026
64
Parents
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Missing Modalities Imputation via Cascaded Residual Autoencoder
Luan Tran, Xiaoming Liu, Jiayu Zhou et al. · 2017 · 227 citations
AutoImpute: Autoencoder based imputation of single-cell RNA-seq data
Divyanshu Talwar, Aanchal Mongia, Debarka Sengupta et al. · Scientific Reports · 2018 · 187 citations · Full text
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Tsinghua University
Beijing, China
3 h-index
4 publications
222 citations
University of California, Irvine
Irvine, United States
2 h-index
5 publications
181 citations
Ministry of Power
New Delhi, India
2 h-index
2 publications
115 citations

Chinese Academy of Sciences
Beijing, China
2 h-index
2 publications
184 citations

City University of Hong Kong
Hong Kong, Hong Kong
2 h-index
3 publications
299 citations
Seoul National University
Seoul, South Korea
2 h-index
4 publications
74 citations

Universidad de Sevilla
Seville, Spain
2 h-index
3 publications
388 citations
Indian Institute of Technology Delhi
New Delhi, India
2 h-index
2 publications
115 citations
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4 publications · 256 citations
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3 publications · 180 citations
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Repository · 3 publications · 515 citations
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2 publications · 115 citations
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2 publications · 87 citations
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