  Most existing temporal point process models are characterized by conditional
intensity function. These models often require numerical approximation methods
for likelihood evaluation, which potentially hurts their performance. By
directly modelling the integral of the intensity function, i.e., the cumulative
hazard function (CHF), the likelihood can be evaluated accurately, making it a
promising approach. However, existing CHF-based methods are not well-defined,
i.e., the mathematical constraints of CHF are not completely satisfied, leading
to untrustworthy results. For multivariate temporal point process, most
existing methods model intensity (or density, etc.) functions for each variate,
limiting the scalability. In this paper, we explore using neural networks to
model a flexible but well-defined CHF and learning the multivariate temporal
point process with low parameter complexity. Experimental results on six
datasets show that the proposed model achieves the state-of-the-art performance
on data fitting and event prediction tasks while having significantly fewer
parameters and memory usage than the strong competitors. The source code and
data can be obtained from https://github.com/lbq8942/NPP.
