A plug-in, SmoothOperator (SmoothOP), which sets the smoothing coefficient of each sample from its prominence, an embedding-space signal measuring how clearly the sample's own class stands out against its strongest competing class, integrates into four existing spherical representation learning methods at minimal train...
CORDIAL is proposed, which treats the model's output as a noisy reading of the true label and corrects it with a channel of five interpretable parameters, and it is proved that the resulting calibration preserves first-order stochastic order.
Xiang-Wei Wang, Peng Wang, Saman K. Halgamuge· 0 citations
Despite its weaker standalone accuracy, the reverse posterior serves as a more useful anchor than forward-only alternatives, providing complementary information for collective decision-making.
Ken Chen, Wei Wang, Sachith Seneviratne et al.· 0 citations
Short-term photovoltaic (PV) power and global horizontal irradiance (GHI) forecasts are essential for effective dispatch, reserve scheduling, and grid operations. At these forecasting horizons, errors are predominantly driven by cloud induced ramps: relying solely on historical numerical data may struggle to anticipate...
This work proposes FedLAS: Feature-Modulated Bidirectional Label Smoothing, a plug-and-play algorithm for label smoothing-based losses that consistently improves calibration compared to modern baselines, reducing Expected Calibration Error (ECE) and Adaptive ECE while maintaining Top-1 accuracy.
Thiru Thillai Nadarasar Bahavan, Sachith Seneviratne, Saman K. Halgamuge· arXiv.org· 0 citations
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