A Recurrent Latent Variable Model for Sequential Data

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In this paper, we explore the inclusion of latent random variables into the dynamic hidden state of a recurrent neural network (RNN) by combining 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 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 dynamic hidden state.