  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.
