  The need for consistent treatment of uncertainty has recently triggered
increased interest in probabilistic deep learning methods. However, most
current approaches have severe limitations when it comes to inference, since
many of these models do not even permit to evaluate exact data likelihoods.
Sum-product networks (SPNs), on the other hand, are an excellent architecture
in that regard, as they allow to efficiently evaluate likelihoods, as well as
arbitrary marginalization and conditioning tasks. Nevertheless, SPNs have not
been fully explored as serious deep learning models, likely due to their
special structural requirements, which complicate learning. In this paper, we
make a drastic simplification and use random SPN structures which are trained
in a "classical deep learning manner", i.e. employing automatic
differentiation, SGD, and GPU support. The resulting models, called RAT-SPNs,
yield prediction results comparable to deep neural networks, while still being
interpretable as generative model and maintaining well-calibrated
uncertainties. This property makes them highly robust under missing input
features and enables them to naturally detect outliers and peculiar samples.
