Temporal point process as the stochastic process on continuous domain of time is usually used to model the asynchronous event sequence featuring with occurence timestamps. With the rise of deep learning, due to the strong expressivity of deep neural networks, they are emerging as a promising choice for capturing the patterns in asynchronous sequences, in the setting 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 as 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. Discrete graph structure learning in the framework of Variational Inference is employed to reveal latent structures of Granger causality graph, and further experiments shows the proposed framework with learned latent graph can both capture the relations and achieve an improved fitting and predicting performance.
翻译:时间点过程是连续时间域的随机过程,通常用来模拟以时标发生时标为特征的无同步事件序列。随着深神经网络的强烈表现,随着深神经网络的强烈表现,深层学习的兴起,它们正在成为在设定时间点过程时,捕捉无同步序列模式的有希望的选择。在本文中,我们首先审查最近研究的重点和困难,模拟具有深时点过程的无同步事件序列,这可以总结为四个领域:历史序列编码、制定有条件强度功能、事件关联发现和优化学习方法。我们引入了大部分最近提出的模型,将其拆为四个部分,并用相同的学习战略对头三个部分进行重新调整,以便公平经验评估。此外,我们扩展历史编码器和有条件强度功能,并提议一个利用多类型事件之间的关系的强烈因果关系发现框架。在Variation Inference框架中进一步学习差异性图表结构,我们采用将最近提出的模型作为四部分,进行实验,同时将头三部分重新组合进行。此外,我们扩大历史编码和有条件的强度功能,提出利用Granger imal imal magistration resmatural construal maturation lacultturation laphing laphal laphal laphal lave laveal lamation laveal laveal lax lax lax 和Gres laveal 和制制制制制制制制制制的图像图,以显示Gres