Q-SRDRN multi-quantile network with pinball loss and per-quantile heads detects extreme precipitation events up to 18 times more effectively than deterministic baselines while preserving augmentation benefits for the median.
Unconstrained monotonic neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
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Exact subset-order-preserving ('MAS') embeddings need dimension ≥|V| on finite ground sets and do not exist for infinite ground sets; the paper relaxes to weakly-MAS hat-activation models with Hölder-stability and probability-of-separation bounds.
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Multi-Quantile Regression for Extreme Precipitation Downscaling
Q-SRDRN multi-quantile network with pinball loss and per-quantile heads detects extreme precipitation events up to 18 times more effectively than deterministic baselines while preserving augmentation benefits for the median.
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Monotone and Separable Set Functions: Characterizations and Neural Models
Exact subset-order-preserving ('MAS') embeddings need dimension ≥|V| on finite ground sets and do not exist for infinite ground sets; the paper relaxes to weakly-MAS hat-activation models with Hölder-stability and probability-of-separation bounds.