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Natasha 2: Faster Non-Convex Optimization Than SGD. (arXiv:1708.08694v2 [math.OC] UPDATED)

来源于:arXiv
We design a stochastic algorithm to train any smooth neural network to $\varepsilon$-approximate local minima, using $O(\varepsilon^{-3.25})$ backpropagations. The best result was essentially $O(\varepsilon^{-4})$ by SGD. More broadly, it finds $\varepsilon$-approximate local minima of any smooth nonconvex function in rate $O(\varepsilon^{-3.25})$, with only oracle access to stochastic gradients and Hessian-vector products. 查看全文>>