The Connection between Kriging and Large Neural Networks
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Abstract (arXiv)
AI has impacted many disciplines and is nowadays ubiquitous. In particular, spatial statistics is in a pivotal moment where it will increasingly intertwine with AI. In this scenario, a relevant question is what relationship spatial statistics models have with machine learning (ML) models, if any. In particular, in this paper, we explore the connections between Kriging and neural networks. At first glance, they may appear unrelated. Kriging - and its ML counterpart, Gaussian process regression - are grounded in probability theory and stochastic processes, whereas many ML models are extensively considered Black-Box models. Nevertheless, they are strongly related. We study their connections and revisit the relevant literature. The understanding of their relations and the combination of both perspectives may enhance ML techniques by making them more interpretable, reliable, and spatially aware.
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References · 6
- 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,621
- 2018On Lazy Training in Differentiable Programmingcited by 675
- 2017Deep Neural Networks as Gaussian Processescited by 622
- 2019Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processescited by 151
- 2018Deep Convolutional Networks as shallow Gaussian Processescited by 138
- 2024Explaining and Connecting Kriging with Gaussian Process Regressioncited by 1
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