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.
AUTHORS
Written by
Ricky T. Q. Chen
Brandon Amos
Maximilian Nickel
Publisher
ICLR 2021
Research Topics
Core Machine Learning
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