2019
Variational Autoencoder with Arbitrary Conditioning
ICLR 2019poster
We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in "one shot". The features may be both real-valued and categorical. Training of the model is performed by stocha…