Computer Science > Machine Learning
arXiv:2306.09463 (cs)
[Submitted on 15 Jun 2023]
Title:Kriging Convolutional Networks
View a PDF of the paper titled Kriging Convolutional Networks, by Gabriel Appleby and 2 other authors
View PDFAbstract:Spatial interpolation is a class of estimation problems where locations with known values are used to estimate values at other locations, with an emphasis on harnessing spatial locality and trends. Traditional Kriging methods have strong Gaussian assumptions, and as a result, often fail to capture complexities within the data. Inspired by the recent progress of graph neural networks, we introduce Kriging Convolutional Networks (KCN), a method of combining the advantages of Graph Convolutional Networks (GCN) and Kriging. Compared to standard GCNs, KCNs make direct use of neighboring observations when generating predictions. KCNs also contain the Kriging method as a specific configuration. We further improve the model's performance by adding attention. Empirically, we show that this model outperforms GCNs and Kriging in several applications. The implementation of KCN using PyTorch is publicized at the GitHub repository:this https URL.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2306.09463 [cs.LG] |
| (orarXiv:2306.09463v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2306.09463 arXiv-issued DOI via DataCite | |
| Related DOI: | https://doi.org/10.1609/aaai.v34i04.5716 DOI(s) linking to related resources |
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