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What is Conformalized KANs for Uncertainty Quantification about?

This paper presents Conformalized-KANs, which enhance uncertainty quantification (UQ) in Kolmogorov-Arnold Networks (KANs) by integrating conformal prediction with KAN ensembles to produce calibrated prediction intervals with guaranteed coverage. The authors demonstrate the effectiveness of their approach through extensive numerical experiments, highlighting improvements in reliability and applicability for scientific machine learning applications. The work builds on previous developments in KAN variants, i

Author

qhwjd0622

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EN