<ul><li>Reliable uncertainty estimation is crucial for using neural networks in real-world applications.</li><li>A new approach called CLUE (Calibration via Learning Uncertainty-Error Alignment) has been introduced to align predicted uncertainty with observed error during training.</li><li>CLUE uses a novel loss function to optimize predictive performance and calibration, making it fully differentiable, domain-agnostic, and compatible with standard training pipelines.</li><li>Extensive experiments demonstrate that CLUE achieves superior calibration quality and competitive predictive performance across various tasks and scenarios.</li></ul>

CLUE: Neural Networks Calibration via Learning Uncertainty-Error alignment

Discover more