Deep Belief Networks (DBNs) constitute the prime example of deep learning architectures. They have shown to be promising feature extractors for high-dimensional real world data. This poses many questions about the geometry of the corresponding generative models. Recent research [1,2] has shed light into the geometry of a closely related model; the Restricted Boltzmann Machine. In this talk I...