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Combined First- and Second-order directions for Deep Neural Networks Training
In this work, we consider a novel stochastic optimization algorithm to solve the unconstrained, nonlinear, and non-convex optimization problems arising in the training of deep neural networks. The new algorithm is based on the combination of first- and second-order information, namely, at each step
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