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Differentiable and Scalable Generative Adversarial Models for Data Imputation
[article]
2022
arXiv
pre-print
Data imputation has been extensively explored to solve the missing data problem. The dramatically increasing volume of incomplete data makes the imputation models computationally infeasible in many real-life applications. In this paper, we propose an effective scalable imputation system named SCIS to significantly speed up the training of the differentiable generative adversarial imputation models under accuracy-guarantees for large-scale incomplete data. SCIS consists of two modules,
arXiv:2201.03202v1
fatcat:nhzoo7hixjha5opg5qb6kn2sb4