Research

Core Machine Learning

Neural Spatio-Temporal Point Processes

May 3, 2021

Abstract

We propose a new class of parameterizations for spatio-temporal point processes which leverage Neural ODEs as a computational method and enable flexible, high-fidelity models of discrete events that are localized in continuous time and space. Central to our approach is a combination of recurrent continuous-time neural networks with two novel neural architectures, i.e., Jump and Attentive Continuous-time Normalizing Flows. This approach allows us to learn complex distributions for both the spatial and temporal domain and to condition non-trivially on the observed event history. We validate our models on data sets from a wide variety of contexts such as seismology, epidemiology, urban mobility, and neuroscience.

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AUTHORS

Written by

Ricky T. Q. Chen

Brandon Amos

Maximilian Nickel

Publisher

ICLR 2021

Research Topics

Core Machine Learning

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