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Convolutional Deep Exponential Families

arXiv 2110.14800 ↗First posted 27 Oct 2021stat.ML cs.LGcited by 0

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Abstract (arXiv)

We describe convolutional deep exponential families (CDEFs) in this paper. CDEFs are built based on deep exponential families, deep probabilistic models that capture the hierarchical dependence between latent variables. CDEFs greatly reduce the number of free parameters by tying the weights of DEFs. Our experiments show that CDEFs are able to uncover time correlations with a small amount of data.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 11 references that are in the map. First posted on arXiv 27 Oct 2021.

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3 of 11 references.

References · 3

  1. 2014Deep Learning in Neural Networks: An Overviewcited by 770
  2. 2013Black Box Variational Inferencecited by 319
  3. 2014Deep Exponential Familiescited by 12

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