  Due to complex experimental settings, missing values are common in biomedical
data. To handle this issue, many methods have been proposed, from ignoring
incomplete instances to various data imputation approaches. With the recent
rise of deep neural networks, the field of missing data imputation has oriented
towards modelling of the data distribution. This paper presents an approach
based on Monte Carlo dropout within (Variational) Autoencoders which offers not
only very good adaptation to the distribution of the data but also allows
generation of new data, adapted to each specific instance. The evaluation shows
that the imputation error and predictive similarity can be improved with the
proposed approach.
