We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised learning of latent Markovian state space models. Leveraging recent advances in Stochastic Gradient Variational Bayes, DVBF can overcome intractable inference distributions by means of 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. * Justin Bayer is also affiliated with sensed.io UG (haftungsbeschränkt),
Deep Variational Bayes Filters (DVBFs) REF enhance the interpretability of DKFs by modelling state transitions with parametric -e.g.
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
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