Neural layers as stationary Schrödinger dynamics on latent graphs are shown equivalent to global supra-graph stationary systems, with coinciding hypothesis classes under strong-monotonicity assumptions and complexity bounds from graph geometry.
By the nerve lemma (Edelsbrun- ner and Harer 2010):
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Learning Latent Graph Geometry via Fixed-Point Schr\"odinger-Type Activation: A Theoretical Study
Neural layers as stationary Schrödinger dynamics on latent graphs are shown equivalent to global supra-graph stationary systems, with coinciding hypothesis classes under strong-monotonicity assumptions and complexity bounds from graph geometry.