  In this paper we propose a model that combines the strengths of RNNs and
SGVB: the Variational Recurrent Auto-Encoder (VRAE). Such a model can be used
for efficient, large scale unsupervised learning on time series data, mapping
the time series data to a latent vector representation. The model is
generative, such that data can be generated from samples of the latent space.
An important contribution of this work is that the model can make use of
unlabeled data in order to facilitate supervised training of RNNs by
initialising the weights and network state.
