Learning deep autoregressive models for hierarchical data
Machine Learning
2021-07-02 v3 Systems and Control
Systems and Control
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.
Cite
@article{arxiv.2104.13853,
title = {Learning deep autoregressive models for hierarchical data},
author = {Carl R. Andersson and Niklas Wahlström and Thomas B. Schön},
journal= {arXiv preprint arXiv:2104.13853},
year = {2021}
}