A Stochastic Variance Reduced Nesterov's Accelerated Quasi-Newton Method

--Recently algorithms incorporating second order curvature information have become popular in training neural networks. The Nesterov's Accelerated Quasi-Newton (NAQ) method has shown to effectively accelerate the BFGS quasi-Newton method by incorporating the momentum term and Nesterov's accelerated gradient vector . A stochastic version of NAQ method was proposed for training of large-scale problems. This paper proposes a stochastic variance reduced Nesterov's Accelerated Quasi-Newton method in full (SVR-NAQ) and limited (SVR-LNAQ) memory forms. The performance of the proposed method is evaluated in T ensorflow on four benchmark problems - two regression and two classification problems respectively. The results show improved performance compared to conventional methods.