  We explore the effects of architecture and training objective choice on
amortized posterior predictive inference in probabilistic conditional
generative models. We aim this work to be a counterpoint to a recent trend in
the literature that stresses achieving good samples when the amount of
conditioning data is large. We instead focus our attention on the case where
the amount of conditioning data is small. We highlight specific architecture
and objective choices that we find lead to qualitative and quantitative
improvement to posterior inference in this low data regime. Specifically we
explore the effects of choices of pooling operator and variational family on
posterior quality in neural processes. Superior posterior predictive samples
drawn from our novel neural process architectures are demonstrated via image
completion/in-painting experiments.
