Related papers: Learning deep autoregressive models for hierarchical data

Learning deep autoregressive models for hierarchical data

URL: http://arxiv.org/abs/2104.13853v1
Date: Wed, 28 Apr 2021 15:58:45 GMT
Title: Learning deep autoregressive models for hierarchical data
Authors: Carl R. Andersson, Niklas Wahlstr\"om, Thomas B. Sch\"on
Abstract summary: We propose a model for hierarchical structured data as an extension to the temporal convolutional network (STCN) We evaluate the proposed model on two different types of sequential data: speech and handwritten text.
Score: 0.6445605125467573
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We propose a model for hierarchical structured data as an extension to the stochastic temporal convolutional network (STCN). 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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