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.
Nature Reviews Methods Primers , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
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A 3D pattern-matching model using Earth Mover's Distance on conflict data outperforms the VIEWS ensemble benchmark in predicting fatalities.
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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.
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The geometry of conflict : 3D Spatio-temporal patterns in fatalities prediction
A 3D pattern-matching model using Earth Mover's Distance on conflict data outperforms the VIEWS ensemble benchmark in predicting fatalities.