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.
IEEE transactions on knowledge and data engineering , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
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