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Found 3 papers, 1 papers with code
Variational auto-encoders with Student's t-prior
We propose a new structure for the variational auto-encoders (VAEs) prior, with the weakly informative multivariate Student's t-distribution.
Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems
Dealing with missing data in data analysis is inevitable.
The advantage of using Student's t-priors in variational autoencoders
Is it optimal to use the standard Gaussian prior in variational autoencoders?
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