Too Sharp, Too Sure: When Calibration Follows Curvature

Researchers have identified a strong link between model calibration, curvature, and margins during the training of deep neural networks. Their findings indicate that Expected Calibration Error closely follows curvature-based sharpness throughout the optimization process. By introducing a margin-aware training objective that targets robust-margin tails and local smoothness, they achieved better out-of-sample calibration without compromising accuracy. AI

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IMPACT Improves understanding of how to achieve better model calibration during training, potentially leading to more reliable AI systems.

RANK_REASON Academic paper on neural network calibration and training dynamics.

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• Pierfrancesco Beneventano
• arXiv

• paper
• safety