Training Neural Networks with Stochastic Hessian-Free Optimization

16 Jan 2013 Ryan Kiros

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... (read more)

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