  Recent progress on deep learning relies heavily on the quality and efficiency
of training algorithms. In this paper, we develop a fast training method
motivated by the nonlinear Conjugate Gradient (CG) framework. We propose the
Conjugate Gradient with Quadratic line-search (CGQ) method. On the one hand, a
quadratic line-search determines the step size according to current loss
landscape. On the other hand, the momentum factor is dynamically updated in
computing the conjugate gradient parameter (like Polak-Ribiere). Theoretical
results to ensure the convergence of our method in strong convex settings is
developed. And experiments in image classification datasets show that our
method yields faster convergence than other local solvers and has better
generalization capability (test set accuracy). One major advantage of the paper
method is that tedious hand tuning of hyperparameters like the learning rate
and momentum is avoided.
