  The loss function of deep networks is known to be non-convex but the precise
nature of this nonconvexity is still an active area of research. In this work,
we study the loss landscape of deep networks through the eigendecompositions of
their Hessian matrix. In particular, we examine how important the negative
eigenvalues are and the benefits one can observe in handling them
appropriately.
