aiqbench analysis

This article investigates spectral bias in neural networks by proposing a sequential fitting framework that challenges standard interpretations found in previous literature. It offers theoretical insights into how deep learning models prioritize low-frequency components during training, which is central to understanding the fundamental mechanics of machine learning architectures.