A Practitioner's Guide to Kolmogorov-Arnold Networks

📝 Original Info

• Title: A Practitioner’s Guide to Kolmogorov-Arnold Networks
• ArXiv ID: 2510.25781
• Date: 2025-10-28
• Authors: ** 정보 없음 (제공된 텍스트에 저자 정보가 포함되어 있지 않음) **

📝 Abstract

Kolmogorov-Arnold Networks (KANs), whose design is inspired-rather than dictated-by the Kolmogorov superposition theorem, have emerged as a structured alternative to MLPs. This review provides a systematic and comprehensive overview of the rapidly expanding KAN literature. The review is organized around three core themes: (i) clarifying the relationships between KANs and Kolmogorov superposition theory (KST), MLPs, and classical kernel methods; (ii) analyzing basis functions as a central design axis; and (iii) summarizing recent advances in accuracy, efficiency, regularization, and convergence. Finally, we provide a practical "Choose-Your-KAN" guide and outline open research challenges and future directions. The accompanying GitHub repository serves as a structured reference for ongoing KAN research.

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