  A temporal point process (TPP) is a stochastic process where its realization
is a sequence of discrete events in time. Recent work in TPPs model the process
using a neural network in a supervised learning framework, where a training set
is a collection of all the sequences. In this work, we propose to train TPPs in
a meta learning framework, where each sequence is treated as a different task,
via a novel framing of TPPs as neural processes (NPs). We introduce context
sets to model TPPs as an instantiation of NPs. Motivated by attentive NP, we
also introduce local history matching to help learn more informative features.
We demonstrate the potential of the proposed method on popular public benchmark
datasets and tasks, and compare with state-of-the-art TPP methods.
