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Distributed Newton Methods for Deep Neural Networks
[article]
2018
arXiv
pre-print
Deep learning involves a difficult non-convex optimization problem with a large number of weights between any two adjacent layers of a deep structure. To handle large data sets or complicated networks, distributed training is needed, but the calculation of function, gradient, and Hessian is expensive. In particular, the communication and the synchronization cost may become a bottleneck. In this paper, we focus on situations where the model is distributedly stored, and propose a novel
arXiv:1802.00130v1
fatcat:zyxjrh2xszbhndcl7oidqycu64