Spectral Indistinguishability and Learning-Curve Identifiability in Quadratic Networks
Read the original on arXiv Machine Learning →The paper investigates how spectral summaries and scalar learning curves can reveal properties of centered quadratic networks. By constructing analytic positive input densities with Gaussian one- and two-coordinate marginals, the authors show that certain teachers with a nonzero cycle invariant produce distinct factorized loss curves, while others yield identical anchored spectral measures. The study further explores the sample complexity required for noisy scalar loss queries and bounded initialization interventions, identifies conditions under which a single scalar loss curve can recover the initial predictor and teacher spectrum, and provides exact recovery guarantees for fixed dimensions and distinct teacher sums.
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