Recent advancements in generative modeling have made it possible to generate
high-quality content from context information, but a key question remains: how
to teach models to know when to generate content? To answer this question, this
study proposes a novel event generative model that draws its statistical
intuition from marked temporal point processes, and offers a clean, flexible,
and computationally efficient solution for a wide range of applications
involving multi-dimensional marks. We aim to capture the distribution of the
point process without explicitly specifying the conditional intensity or
probability density. Instead, we use a conditional generator that takes the
history of events as input and generates the high-quality subsequent event that
is likely to occur given the prior observations. The proposed framework offers
a host of benefits, including exceptional efficiency in learning the model and
generating samples, as well as considerable representational power to capture
intricate dynamics in multi- or even high-dimensional event space. Our
numerical results demonstrate superior performance compared to other
state-of-the-art baselines.

本研究提出了一种基于被标记的时间点过程的新事件生成模型，其通过条件生成器以及多维标记来捕捉点过程的分布，从而在多维事件空间中实现了出色的学习效率和样本生成性能的表现，并且通过在数值实验中对比其他最先进基线表明其性能优于其他方法。