AI RESEARCH
Two-Dimensional Deep ReLU CNN Approximation for Korobov Functions: A Constructive Approach
arXiv CS.LG
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ArXi:2503.07976v2 Announce Type: replace-cross This paper investigates approximation capabilities of two-dimensional (2D) deep convolutional neural networks (CNNs), with Korobo functions serving as a benchmark. We focus on 2D CNNs, comprising multi-channel convolutional layers with zero-padding and ReLU activations, followed by a fully connected layer. We propose a fully constructive approach for building 2D CNNs to approximate Korobo functions and provide a rigorous analysis of the complexity of the constructed networks.