Bilevel optimization is applied to neural network training to produce self-calibrated confidence scores, reducing calibration error versus isotonic regression on toy and simulated BAC datasets while preserving accuracy.
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Exploring the Potential of Bilevel Optimization for Calibrating Neural Networks
Bilevel optimization is applied to neural network training to produce self-calibrated confidence scores, reducing calibration error versus isotonic regression on toy and simulated BAC datasets while preserving accuracy.