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Title: Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities
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.  more » « less
Award ID(s):
1737770
PAR ID:
10179447
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Prof. Int. Conf. Learning Representations (ICLR)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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