  Data quality problems are a large threat in data science. In this paper, we
propose a data-cleaning autoencoder capable of near-automatic data quality
improvement. It learns the structure and dependencies in the data and uses it
as evidence to identify and correct doubtful values. We apply a probabilistic
database approach to represent weak and strong evidence for attribute value
repairs. A theoretical framework is provided, and experiments show that it can
remove significant amounts of noise (i.e., data quality problems) from
categorical and numeric probabilistic data. Our method does not require clean
data. We do, however, show that manually cleaning a small fraction of the data
significantly improves performance.
