  We introduce Deep Variational Bayes Filters (DVBF), a new method for
unsupervised learning and identification of latent Markovian state space
models. Leveraging recent advances in Stochastic Gradient Variational Bayes,
DVBF can overcome intractable inference distributions via variational
inference. Thus, it can handle highly nonlinear input data with temporal and
spatial dependencies such as image sequences without domain knowledge. Our
experiments show that enabling backpropagation through transitions enforces
state space assumptions and significantly improves information content of the
latent embedding. This also enables realistic long-term prediction.
