A recursive cubing framework identifies stable hyperparameter regions for MC dropout uncertainty quantification in spatial deep learning and produces competitive or superior predictive intervals versus a statistical baseline on simulations and land-surface temperature data.
Fixed--width output analysis for
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A random scale mixture process with amortized Bayesian inference enables scalable modeling of spatially dependent extreme temperatures and associated heat risks.
citing papers explorer
-
A Cubing Strategy for Identifying Stable Hyperparameter Regions for Uncertainty Quantification in Spatial Deep Learning
A recursive cubing framework identifies stable hyperparameter regions for MC dropout uncertainty quantification in spatial deep learning and produces competitive or superior predictive intervals versus a statistical baseline on simulations and land-surface temperature data.
-
Spatial Extremes at Scale: A Case Study of Surface Skin Temperature and Heat Risk in the United States
A random scale mixture process with amortized Bayesian inference enables scalable modeling of spatially dependent extreme temperatures and associated heat risks.