Deep Neural Network Learning with Second-Order Optimizers -- a Practical Study with a Stochastic Quasi-Gauss-Newton Method

6 Apr 2020Christopher ThieleMauricio Araya-PoloDetlef Hohl

Training in supervised deep learning is computationally demanding, and the convergence behavior is usually not fully understood. We introduce and study a second-order stochastic quasi-Gauss-Newton (SQGN) optimization method that combines ideas from stochastic quasi-Newton methods, Gauss-Newton methods, and variance reduction to address this problem... (read more)

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