Title: Universal Conditional Machine,
Abstract: 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 stochastic
variational Bayes. The experimental evaluation on synthetic data, as well as
feature imputation and image inpainting problems, shows the effectiveness of
the proposed approach and diversity of the generated samples.