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interpretable function approximation

topic1 events
papersTODAY 04:00 UTC

Method restores distance-awareness guarantees in spline-based Kolmogorov-Arnold networks

A new arXiv preprint addresses a limitation in DAREK, a computationally cheap bottom-up scheme for estimating uncertainty in Kolmogorov-Arnold Networks that use spline activations. The authors describe the problem as "fictitious knots" that weaken distance-awareness guarantees in high-dimensional settings, and propose a fix to restore them. The work targets interpretable function approximation, where reliable uncertainty estimates matter for trusting model outputs.

research/uncertainty/kan/arxiv