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MIWAE: Deep Generative Modelling and Imputation of Incomplete Data
[article]
2019
arXiv
pre-print
We consider the problem of handling missing data with deep latent variable models (DLVMs). First, we present a simple technique to train DLVMs when the training set contains missing-at-random data. Our approach, called MIWAE, is based on the importance-weighted autoencoder (IWAE), and maximises a potentially tight lower bound of the log-likelihood of the observed data. Compared to the original IWAE, our algorithm does not induce any additional computational overhead due to the missing data. We
arXiv:1812.02633v2
fatcat:rouuxoskcndetectdxpqjyds3u