Deep neural networks from the perspective of ergodic theory

The Hessian at critical points have many eigenvalues in high parameter dimensions, andit ismorelikelythat theytakeonboth positive Artificial neural networks have demonstrated great potential and negative values giving us saddle points, so we are in their ability to learn existing knowledge, and interpolate less likely to be trapped, and could instead slip out along or even slightly extrapolate to new situations.