Neural Temporal Point Processes: A Review
It provides a comprehensive overview for researchers working on temporal event modeling, but it is incremental as it reviews existing work rather than introducing new methods.
This review paper consolidates existing knowledge on neural temporal point processes, which combine probabilistic generative models for continuous-time event sequences with deep learning to create flexible and efficient models, and it outlines design choices, applications, and open challenges in the field.
Temporal point processes (TPP) are probabilistic generative models for continuous-time event sequences. Neural TPPs combine the fundamental ideas from point process literature with deep learning approaches, thus enabling construction of flexible and efficient models. The topic of neural TPPs has attracted significant attention in the recent years, leading to the development of numerous new architectures and applications for this class of models. In this review paper we aim to consolidate the existing body of knowledge on neural TPPs. Specifically, we focus on important design choices and general principles for defining neural TPP models. Next, we provide an overview of application areas commonly considered in the literature. We conclude this survey with the list of open challenges and important directions for future work in the field of neural TPPs.