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• W2963746765 abstract "Abstract: Hessian-free (HF) optimization has been successfully used for training deep autoencoders and recurrent networks. HF uses the conjugate gradient algorithm to construct update directions through curvature-vector products that can be computed on the same order of time as gradients. In this paper we exploit this property and study stochastic HF with gradient and curvature mini-batches independent of the dataset size. We modify Martens' HF for these settings and integrate dropout, a method for preventing co-adaptation of feature detectors, to guard against overfitting. Stochastic Hessian-free optimization gives an intermediary between SGD and HF that achieves competitive performance on both classification and deep autoencoder experiments." @default.
• W2963746765 created "2019-07-30" @default.
• W2963746765 creator A5055166817 @default.
• W2963746765 date "2013-01-16" @default.
• W2963746765 modified "2023-09-24" @default.
• W2963746765 title "Training Neural Networks with Stochastic Hessian-Free Optimization" @default.
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• W2963746765 hasPublicationYear "2013" @default.
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