Neural Temporal Point Processes: A Review

Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Stephan Günnemann

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

28 Zitate (Scopus)

Abstract

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 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.

OriginalspracheEnglisch
TitelProceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021
Redakteure/-innenZhi-Hua Zhou
Herausgeber (Verlag)International Joint Conferences on Artificial Intelligence
Seiten4585-4593
Seitenumfang9
ISBN (elektronisch)9780999241196
PublikationsstatusVeröffentlicht - 2021
Veranstaltung30th International Joint Conference on Artificial Intelligence, IJCAI 2021 - Virtual, Online, Kanada
Dauer: 19 Aug. 202127 Aug. 2021

Publikationsreihe

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Konferenz

Konferenz30th International Joint Conference on Artificial Intelligence, IJCAI 2021
Land/GebietKanada
OrtVirtual, Online
Zeitraum19/08/2127/08/21

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