Differentiable and Scalable Generative Adversarial Models for Data Imputation-Reference-Cited by-同舟云学术

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Differentiable and Scalable Generative Adversarial Models for Data Imputation

Published:2023 Issue: Volume: Page:1-13
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Wu Yangyang¹^ORCID,Wang Jun²^ORCID,Miao Xiaoye²,Wang Wenjia²,Yin Jianwei³^ORCID

Affiliation:

1. Center for Data Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, P. R. China

2. Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

3. Center for Data Science, the College of Computer Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, P. R. China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/4358933/10175548.pdf?arnumber=10175548

Reference48 articles.

1. Generative Semi-supervised Learning for Multivariate Time Series Imputation

3. Jointly Imputing Multi-View Data with Optimal Transport

4. Missing Value Imputation for Multi-view Urban Statistical Data via Spatial Correlation Learning

5. "Deep" Learning for Missing Value Imputationin Tables with Non-Numerical Data

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Missing Data Imputation with Uncertainty-Driven Network;Proceedings of the ACM on Management of Data;2024-05-29

2. Differentiable and Scalable Generative Adversarial Models for Data Imputation (Extended Abstract);2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

3. Outlier Summarization via Human Interpretable Rules;Proceedings of the VLDB Endowment;2024-03

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