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Kolmogorov-Arnold stability for discontinuous functions explored in new paper

By PulseAugur Editorial · [1 sources] ·

A new paper explores the stability of the Kolmogorov-Arnold representation theorem (KART) when applied to discontinuous and unbounded functions. The research specifically investigates how adversarial reparameterizations of hidden layers affect this stability. The findings aim to provide a stronger mathematical basis for the resilience of deep learning architectures, such as Kolmogorov-Arnold Networks (KANs), against adversarial configurations. AI

IMPACT Provides theoretical grounding for the robustness of deep learning architectures against adversarial attacks.

RANK_REASON The cluster contains an academic paper on a theoretical aspect of machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Kolmogorov-Arnold stability for discontinuous functions explored in new paper

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The cluster contains an academic paper on a theoretical aspect of machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sviatoslav V. Dzhenzher ·

    Kolmogorov--Arnold stability for discontinuous functions

    arXiv:2609.07240v1 Announce Type: new Abstract: Here we investigate the stability of the Kolmogorov--Arnold representation theorem (KART) under adversarial reparameterisations of the hidden layer for multivariate discontinuous and unbounded functions. Our results provide a rigoro…

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