DVBF: Deep Variational Bayes Filtering
DVBF is a method, based on variational inference, which can represent time series data in regularised latent spaces. Our first paper on DVBF describes the theory and its use as deep Kalman filter, creating physically useful latent spaces from high-dimensional observed data.
DVBF and DMPs
DVBF can be combined with Dynamic Movement Primitives (DMPs) to obtain robust movement representation. In this paper we explain how not only the variational autoencoder parameters but also the DMP parameters are learned.