A prism hierarchy of learning regimes in large linear autoencoders
The study systematically classifies learning dynamics for large linear autoencoders into five extreme regimes using a loss-expansion hierarchy derived from diagrammatic analysis. It maps these regimes onto the faces of a triangular prism, defining distinct behaviors based on input size, latent dimension, and training set size. This provides a structured framework to understand how models learn under different data constraints.