In this paper, we explore the inclusion of latent random variables into the hidden state of a recurrent neural network (RNN) by combining the elements of the variational autoencoder. We argue that through the use of high-level latent random variables, the variational RNN (VRNN) 1 can model the kind of variability observed in highly structured sequential data such as natural speech. We empirically evaluate the proposed model against other related sequential models on four speech datasets and one handwriting dataset. Our results show the important roles that latent random variables can play in the RNN dynamics.
This can be overcome by the use of Variational RNNs (VRNNs) to generate consistent handwriting REF .
A Recurrent Latent Variable Model for Sequential Data
ArXiv
ArXiv
Mathematics Computer Science