ICLR 2020poster15 citations
Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities
Baichuan Yuan, Xiaowei Wang, Jianxin Ma, Chang Zhou, Andrea L. Bertozzi, Hongxia Yang
Abstract
Multivariate spatial point process models can describe heterotopic data over space. However, highly multivariate intensities are computationally challenging due to the curse of dimensionality. To bridge this gap, we introduce a declustering based hidden variable model that leads to an efficient inference procedure via a variational autoencoder (VAE). We also prove that this model is a generalization of the VAE-based model for collaborative filtering. This leads to an interesting application of spatial point process models to recommender systems. Experimental results show the method's utility on both synthetic data and real-world data sets.
VAEcollaborative filteringrecommender systemsspatial point process
BibTeX
@inproceedings{
Yuan2020Variational,
title={Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities},
author={Baichuan Yuan and Xiaowei Wang and Jianxin Ma and Chang Zhou and Andrea L. Bertozzi and Hongxia Yang},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=B1lj20NFDS}
}