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

Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data