2020
Stochastic Deep Gaussian Processes over Graphs
NeurIPS 2020poster
In this paper we propose Stochastic Deep Gaussian Processes over Graphs (DGPG), which are deep structure models that learn the mappings between input and output signals in graph domains. The approximate posterior distributions of the latent variables are derived with variational inference, and the e…