  We propose a model for hierarchical structured data as an extension to the
stochastic temporal convolutional network. The proposed model combines an
autoregressive model with a hierarchical variational autoencoder and
downsampling to achieve superior computational complexity. We evaluate the
proposed model on two different types of sequential data: speech and
handwritten text. The results are promising with the proposed model achieving
state-of-the-art performance.
