Approximation Power of Neural Networks with GELU: A Deep Dive

This ArXiv paper likely explores the theoretical properties of feedforward neural networks utilizing the Gaussian Error Linear Unit (GELU) activation function, a common choice in modern architectures. Understanding these approximation capabilities can provide insights into network design and efficiency for various machine learning tasks.

• •Investigates the theoretical ability of networks with GELU activation to approximate complex functions.
• •Potentially provides guidance on network architecture choices, such as layer depth and width.
• •Contributes to the understanding of the expressiveness of GELU-based neural networks.