  Neural processes (NPs) constitute a family of variational approximate models
for stochastic processes with promising properties in computational efficiency
and uncertainty quantification. These processes use neural networks with latent
variable inputs to induce predictive distributions. However, the expressiveness
of vanilla NPs is limited as they only use a global latent variable, while
target specific local variation may be crucial sometimes. To address this
challenge, we investigate NPs systematically and present a new variant of NP
model that we call Doubly Stochastic Variational Neural Process (DSVNP). This
model combines the global latent variable and local latent variables for
prediction. We evaluate this model in several experiments, and our results
demonstrate competitive prediction performance in multi-output regression and
uncertainty estimation in classification.
