  Neural Processes (NPs) consider a task as a function realized from a
stochastic process and flexibly adapt to unseen tasks through inference on
functions. However, naive NPs can model data from only a single stochastic
process and are designed to infer each task independently. Since many
real-world data represent a set of correlated tasks from multiple sources
(e.g., multiple attributes and multi-sensor data), it is beneficial to infer
them jointly and exploit the underlying correlation to improve the predictive
performance. To this end, we propose Multi-Task Neural Processes (MTNPs), an
extension of NPs designed to jointly infer tasks realized from multiple
stochastic processes. We build MTNPs in a hierarchical way such that inter-task
correlation is considered by conditioning all per-task latent variables on a
single global latent variable. In addition, we further design our MTNPs so that
they can address multi-task settings with incomplete data (i.e., not all tasks
share the same set of input points), which has high practical demands in
various applications. Experiments demonstrate that MTNPs can successfully model
multiple tasks jointly by discovering and exploiting their correlations in
various real-world data such as time series of weather attributes and
pixel-aligned visual modalities. We release our code at
https://github.com/GitGyun/multi_task_neural_processes.
