  We propose a new probabilistic method for unsupervised recovery of corrupted
data. Given a large ensemble of degraded samples, our method recovers accurate
posteriors of clean values, allowing the exploration of the manifold of
possible reconstructed data and hence characterising the underlying
uncertainty. In this setting, direct application of classical variational
methods often gives rise to collapsed densities that do not adequately explore
the solution space. Instead, we derive our novel reduced entropy condition
approximate inference method that results in rich posteriors. We test our model
in a data recovery task under the common setting of missing values and noise,
demonstrating superior performance to existing variational methods for
imputation and de-noising with different real data sets. We further show higher
classification accuracy after imputation, proving the advantage of propagating
uncertainty to downstream tasks with our model.

Machine Learning;Machine Learning