Negative eigenvalues of the Hessian in deep neural networks
Guillaume Alain ⋅ Nicolas Le Roux ⋅ Pierre-Antoine Manzagol
Abstract
We study the loss function of a deep neural network through the eigendecomposition of its Hessian matrix. We focus on negative eigenvalues, how important they are, and how to best deal with them. The goal is to develop an optimization method specifically tailored for deep neural networks.
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