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We derive a sound positive semi-definite approximation of the Hessian of deepmodels for which Hessian-vector products are easily computable. This enables usto provide an adaptive SGD learning rate strategy based on the minimization ofthe local quadratic approximation, which requires just twice the computation ofa single SGD run, but performs comparably with grid search on SGD learningrates on different model architectures (CNN with and without residualconnections) on classification tasks. We also compare the novel approximationwith the Gauss-Newton approximation.

ADLER -- An efficient Hessian-based strategy for adaptive learning rate