Theoretical Insights into Deep Classifiers and Neural Networks

This chapter explores recent theoretical advancements in deep classifiers and their application in AI, with a focus on normalization and neural collapse. The discussion highlights the complexities of neural networks and the necessity of foundational research for practical interpretations in AI policy and capabilities.