Eigenvalues of the Hessian in Deep Learning: The Origin of Symmetry and Its Breaking

Hessian spectra at trained models in deep learning exhibit a persistent pattern: eigenvalues organize into distinct clusters, including a large bulk near zero and a few isolated outliers. This paper shows that a natural account of these spectral phenomena emerges when the original setting is underst