  Temporal point process as the stochastic process on continuous domain of time
is commonly used to model the asynchronous event sequence featuring with
occurrence timestamps. Thanks to the strong expressivity of deep neural
networks, they are emerging as a promising choice for capturing the patterns in
asynchronous sequences, in the context of temporal point process. In this
paper, we first review recent research emphasis and difficulties in modeling
asynchronous event sequences with deep temporal point process, which can be
concluded into four fields: encoding of history sequence, formulation of
conditional intensity function, relational discovery of events and learning
approaches for optimization. We introduce most of recently proposed models by
dismantling them into the four parts, and conduct experiments by remodularizing
the first three parts with the same learning strategy for a fair empirical
evaluation. Besides, we extend the history encoders and conditional intensity
function family, and propose a Granger causality discovery framework for
exploiting the relations among multi-types of events. Because the Granger
causality can be represented by the Granger causality graph, discrete graph
structure learning in the framework of Variational Inference is employed to
reveal latent structures of the graph. Further experiments show that the
proposed framework with latent graph discovery can both capture the relations
and achieve an improved fitting and predicting performance.
