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Learning deep autoregressive models for hierarchical data

arXiv 2104.13853 ↗First posted 28 Apr 2021cs.LG cs.SY eess.SYcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 33 references that are in the map. First posted on arXiv 28 Apr 2021.

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References · 20

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8 more
  1. 2016Hierarchical Multiscale Recurrent Neural Networkscited by 102
  2. 2014A Clockwork RNNcited by 70
  3. 2016PixelVAE: A Latent Variable Model for Natural Imagescited by 69
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  8. 2018Stochastic WaveNet: A Generative Latent Variable Model for Sequential Datacited by 4

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