Diffusion Operator Geometry of Feedforward Representations
This work uses diffusion operators to study how class structures evolve in neural networks by assigning smooth Markov operators to feature snapshots. It defines geometric summaries like class transport and local properties directly from these operators, offering a continuous way to analyze feature separation rather than relying on discrete neighborhood graphs.