The spectral neuron: a nonlinear ML primitive that stays coefficient-transparent
The spectral neuron sits between linear models and opaque nets: it reads the prediction from an eigenvalue of an affine input matrix, keeping nonlinearity mathematically explicit. Extrema yield convex or concave functions, semidefinite constraints force monotonicity, and expressiveness grows with matrix dimension. The paper characterizes expressivity, feature influence, and shape control, with code released.