This note addresses the Kolmogorov-Arnold Representation Theorem (KART) and the Universal Approximation Theorem (UAT), focusing on their common misinterpretations in some papers related to neural network approximation. Our remarks aim to support a more accurate understanding of KART and UAT among neural network specialists.

翻译：本文针对Kolmogorov-Arnold表示定理（KART）与通用逼近定理（UAT），重点探讨其在部分神经网络逼近相关研究中的常见误读现象。我们的论述旨在帮助神经网络领域研究者更准确地理解KART与UAT的理论内涵。