Accelerate RNN-based Training with Importance Sampling

31 Oct 2017Fei WangXiaofeng GaoGuihai ChenJun Ye

Importance sampling (IS) as an elegant and efficient variance reduction (VR) technique for the acceleration of stochastic optimization problems has attracted many researches recently. Unlike commonly adopted stochastic uniform sampling in stochastic optimizations, IS-integrated algorithms sample training data at each iteration with respect to a weighted sampling probability distribution $P$, which is constructed according to the precomputed importance factors... (read more)

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