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Found 4 papers, 2 papers with code
Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families
The increased complexity may adversely affect the fit of the model due to a mismatch between the variational and model posterior distributions.
Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling
Conditional sampling of variational autoencoders (VAEs) is needed in various applications, such as missing data imputation, but is computationally intractable.
Learning Job Titles Similarity from Noisy Skill Labels
Measuring semantic similarity between job titles is an essential functionality for automatic job recommendations.
Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data
We address this gap by introducing variational Gibbs inference (VGI), a new general-purpose method to estimate the parameters of statistical models from incomplete data.
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