arXiv stat.ML
· Papers
Can Deep Generative Models Reproduce Non-Stationary Gaussian Random Fields?
arXiv:2607.25929v1 Announce Type: new Abstract: Deep generative models (DGMs) are widely used for complex high-dimensional data and increasingly applied to spatial and spatio-temporal modeling. Their generated samples implicitly represent the learned data distribution and associated uncertainty. However, for real-world