# Convolutional Deep Exponential Families

arXiv: https://arxiv.org/abs/2110.14800 · HTML: https://mlsanity.com/paper/2110.14800/

## 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.

## Builds on (most cited first, 3 of 3)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Black Box Variational Inference](https://mlsanity.com/paper/1401.0118/) (2013)
- [Deep Exponential Families](https://mlsanity.com/paper/1411.2581/) (2014)

## 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.