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...
A large language model (LLM) can turn a text into a distribution over an ordered scale, but that distribution is a noisy measurement: saturated, compressed or exaggerated, and biased in a consistent direction. We propose CORDIAL, which treats the model's output as a noisy reading of the true label and corrects it with...
Xiang-Wei Wang, Peng Wang, Saman K. Halgamuge· 0 citations
When multiple LLM agents yield conflicting answers, the decision-making process dictates whether agent diversity improves performance or merely compounds shared errors. Existing collective decision-making methods, including voting, electoral rules, and LLM judges, rely on forward reasoning: they map evidence to labels...
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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
