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Found 2 papers, 1 papers with code
Combining propensity score methods with variational autoencoders for generating synthetic data in presence of latent sub-groups
The sources of such heterogeneity might be known, e. g., as indicated by sub-groups labels, or might be unknown and thus reflected only in properties of distributions, such as bimodality or skewness.
Adapting deep generative approaches for getting synthetic data with realistic marginal distributions
While there are extensions that assume other distributions for the latent space, this does not generally increase flexibility for data with many different distributions.
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