Accelerating SGD for Distributed Deep-Learning Using Approximated Hessian Matrix

Sébastien M. R. Arnold; Chunming Wang

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arXiv · 1709.05069

Accelerating SGD for Distributed Deep-Learning Using Approximated Hessian Matrix

Sébastien M. R. Arnold·Chunming Wang

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Abstract

We introduce a novel method to compute a rank $m$ approximation of the inverse of the Hessian matrix in the distributed regime. By leveraging the differences in gradients and parameters of multiple Workers, we are able to efficiently implement a distributed approximation of the Newton-Raphson method. We also present preliminary results which underline advantages and challenges of second-order methods for large stochastic optimization problems. In particular, our work suggests that novel strategies for combining gradients provide further information on the loss surface.

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Sébastien M. R. Arnold, Chunming Wang. 2017-09-15. Accelerating SGD for Distributed Deep-Learning Using Approximated Hessian Matrix. https://arxiv.org/abs/1709.05069

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