Derives a posterior-predictive variance decomposition separating epistemic and aleatoric uncertainty in heteroscedastic Bayesian neural network models for wind power forecasting, with a dedicated validation framework tested on synthetic and real SCADA data.
Prioritized training on points that are learnable, worth learning, and not yet learnt
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
RadarPLM adapts PLMs for marine radar target detection with lightweight adaptation and selective fine-tuning based on online learning values, reporting at least 6.35% average detection gains in low SCR conditions.
citing papers explorer
-
A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting
Derives a posterior-predictive variance decomposition separating epistemic and aleatoric uncertainty in heteroscedastic Bayesian neural network models for wind power forecasting, with a dedicated validation framework tested on synthetic and real SCADA data.
-
RadarPLM: Adapting Pre-trained Language Models for Marine Radar Target Detection by Selective Fine-tuning
RadarPLM adapts PLMs for marine radar target detection with lightweight adaptation and selective fine-tuning based on online learning values, reporting at least 6.35% average detection gains in low SCR conditions.