A mean-field limit yields a convex, price-responsive surrogate for aggregated storage that is learned via gradient descent on historical data and converges with population size.
arXiv preprint arXiv:1911.01525 , year=
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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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Mean-Field Learning for Storage Aggregation
A mean-field limit yields a convex, price-responsive surrogate for aggregated storage that is learned via gradient descent on historical data and converges with population size.
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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.