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Geometry-Constrained KANs Learn Adaptive Edge Functions for Symbolic Regression

By PulseAugur Editorial · [2 sources] ·

Researchers have developed geometry-constrained Kolmogorov-Arnold Networks (KANs) that learn edge geometry through a scalar exponent 'p'. This approach allows for adaptive responses, ranging from sharp, threshold-like behavior to flatter curves, depending on the learned exponent. Across 50 symbolic regression tasks, these geometry-constrained KANs, particularly the Banach-KAN variant, matched or surpassed fixed-basis methods in median NRMSE and demonstrated superior stability under measurement noise and in small-sample regimes. The learned exponents also offer interpretable insights into target-dependent geometric ordering. AI

IMPACT Introduces a novel approach to neural network architecture that could improve performance and interpretability in symbolic regression tasks.

RANK_REASON The cluster describes a new research paper detailing a novel method for neural networks.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Geometry-Constrained KANs Learn Adaptive Edge Functions for Symbolic Regression

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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Geometry-Constrained Kolmogorov-Arnold Networks: Learning Edge Geometry via Banach Duality

    Kolmogorov-Arnold Networks (KANs) replace fixed activations in deep architectures with learnable univariate edge functions, making the choice of edge parametrisation central. Existing variants rely on fixed bases such as splines, polynomials, or Fourier features, which impose a f…

  2. arXiv stat.ML TIER_1 English(EN) · K S Sesh Kumar ·

    Geometry-Constrained Kolmogorov-Arnold Networks: Learning Edge Geometry via Banach Duality

    arXiv:2608.25807v1 Announce Type: cross Abstract: Kolmogorov-Arnold Networks (KANs) replace fixed activations in deep architectures with learnable univariate edge functions, making the choice of edge parametrisation central. Existing variants rely on fixed bases such as splines, …

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