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Neural networks can achieve exponential convergence to structured representations, revealing a surprising link between training dynamics and representation theory.
Two-layer neural nets solve modular addition via a "flawed indicator function" built from noisy single-frequency Fourier features, where phase symmetry enables a majority-voting noise cancellation scheme.
