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arXiv · 2104.13853

Learning deep autoregressive models for hierarchical data

Carl R. Andersson·Niklas Wahlström·Thomas B. Schön
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Abstract

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.

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Carl R. Andersson, Niklas Wahlström, Thomas B. Schön. 2021-04-28. Learning deep autoregressive models for hierarchical data. https://arxiv.org/abs/2104.13853

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