  We showed how to use trained neural networks to perform Bayesian reasoning in
order to solve tasks outside their initial scope. Deep generative models
provide prior knowledge, and classification/regression networks impose
constraints. The tasks at hand were formulated as Bayesian inference problems,
which we approximately solved through variational or sampling techniques. The
approach built on top of already trained networks, and the addressable
questions grew super-exponentially with the number of available networks. In
its simplest form, the approach yielded conditional generative models. However,
multiple simultaneous constraints constitute elaborate questions. We compared
the approach to specifically trained generators, showed how to solve riddles,
and demonstrated its compatibility with state-of-the-art architectures.
