Neural Spatio-Temporal Point Processes
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 continuous-time neural networks with two novel neural architectures, \ie, 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.
Cite
Text
Chen et al. "Neural Spatio-Temporal Point Processes." International Conference on Learning Representations, 2021.Markdown
[Chen et al. "Neural Spatio-Temporal Point Processes." International Conference on Learning Representations, 2021.](https://mlanthology.org/iclr/2021/chen2021iclr-neural/)BibTeX
@inproceedings{chen2021iclr-neural,
title = {{Neural Spatio-Temporal Point Processes}},
author = {Chen, Ricky T. Q. and Amos, Brandon and Nickel, Maximilian},
booktitle = {International Conference on Learning Representations},
year = {2021},
url = {https://mlanthology.org/iclr/2021/chen2021iclr-neural/}
}