  Probabilistic models learned as density estimators can be exploited in
representation learning beside being toolboxes used to answer inference queries
only. However, how to extract useful representations highly depends on the
particular model involved. We argue that tractable inference, i.e. inference
that can be computed in polynomial time, can enable general schemes to extract
features from black box models. We plan to investigate how Tractable
Probabilistic Models (TPMs) can be exploited to generate embeddings by random
query evaluations. We devise two experimental designs to assess and compare
different TPMs as feature extractors in an unsupervised representation learning
framework. We show some experimental results on standard image datasets by
applying such a method to Sum-Product Networks and Mixture of Trees as
tractable models generating embeddings.
