  Neural processes are a family of probabilistic models that inherit the
flexibility of neural networks to parameterize stochastic processes. Despite
providing well-calibrated predictions, especially in regression problems, and
quick adaptation to new tasks, the Gaussian assumption that is commonly used to
represent the predictive likelihood fails to capture more complicated
distributions such as multimodal ones. To overcome this limitation, we propose
Conditional Quantile Neural Processes (CQNPs), a new member of the neural
processes family, which exploits the attractive properties of quantile
regression in modeling the distributions irrespective of their form. By
introducing an extension of quantile regression where the model learns to focus
on estimating informative quantiles, we show that the sampling efficiency and
prediction accuracy can be further enhanced. Our experiments with real and
synthetic datasets demonstrate substantial improvements in predictive
performance compared to the baselines, and better modeling of heterogeneous
distributions' characteristics such as multimodality.
