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Found 6 papers, 0 papers with code
A modified limited memory Nesterov's accelerated quasi-Newton
The Nesterov's accelerated quasi-Newton (L)NAQ method has shown to accelerate the conventional (L)BFGS quasi-Newton method using the Nesterov's accelerated gradient in several neural network (NN) applications.
A Nesterov's Accelerated quasi-Newton method for Global Routing using Deep Reinforcement Learning
Deep Q-learning method is one of the most popularly used deep reinforcement learning algorithms which uses deep neural networks to approximate the estimation of the action-value function.
Implementation of a modified Nesterov's Accelerated quasi-Newton Method on Tensorflow
The Nesterov's Accelerated Quasi-Newton (NAQ) method has shown to drastically improve the convergence speed compared to the conventional quasi-Newton method.
A Stochastic Variance Reduced Nesterov's Accelerated Quasi-Newton Method
This paper proposes a stochastic variance reduced Nesterov's Accelerated Quasi-Newton method in full (SVR-NAQ) and limited (SVRLNAQ) memory forms.
An Adaptive Stochastic Nesterov Accelerated Quasi Newton Method for Training RNNs
A common problem in training neural networks is the vanishing and/or exploding gradient problem which is more prominently seen in training of Recurrent Neural Networks (RNNs).
A Stochastic Quasi-Newton Method with Nesterov's Accelerated Gradient
Incorporating second order curvature information in gradient based methods have shown to improve convergence drastically despite its computational intensity.
