Approximation Power of Neural Networks with GELU: A Deep Dive
Research#Neural Networks🔬 Research|Analyzed: Jan 10, 2026 07:19•
Published: Dec 25, 2025 17:56
•1 min read
•ArXivAnalysis
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.
Key Takeaways
- •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.
Reference / Citation
View Original"The study focuses on feedforward neural networks with GELU activations."