Using Second-Order Information in Training Deep Neural Networks

Description

In this dissertation, we are concerned with the advancement of optimization algorithms for training deep learning models, and in particular about practical second-order methods that take into account the structure of deep neural networks (DNNs). Although first-order methods such as stochastic gradient descent have long been the predominant optimization algorithm used in deep learning, second-order methods are of interest because of their ability to use curvature information to accelerate the optimization process. After the presentation of some background information in Chapter 1, Chapters 2 and 3 focus on the development of practical quasi-Newton methods for training DNNs. We analyze the Kronecker-factored structure of the Hessian matrix of multi-layer perceptrons and convolutional neural networks and consequently propose block-diagonal Kronecker-factored quasi-Newton methods named K-BFGS and K-BFGS(L). To handle the non-convexity nature of DNNs, we also establish new double damping techniques for our proposed methods. Our K-BFGS and K-BFGS(L) methods have memory requirements comparable to first-order methods and experience only mild overhead in terms of per-iteration time complexity. In Chapter 4, we develop a new approximate natural gradient method named Tensor Normal Training (TNT), in which the Fisher matrix is viewed as the covariance matrix of a tensor normal distribution (a generalized form of the normal distribution). The tractable Kronecker-factored approximation to the Fisher information matrix that results from this approximation enables TNT to enjoy memory requirements and per-iteration computational costs that are only slightly higher than those for first-order methods. Notably, unlike KFAC and K-BFGS/K-BFGS(L), TNT only requires the knowledge of the shape of the trainable parameters of a model and does not depend on the specific model architecture. In Chapter 5, we consider the subsampled versions of Gauss-Newton and natural gradient methods applied to DNNs. Because of the low-rank nature of the subsampled matrices, we make use of the Sherman-Morrison-Woodbury formula along with backpropagation to efficiently compute their inverse. We also show that, under rather mild conditions, the algorithm converges to a stationary point if Levenberg-Marquardt damping is used. The results of a substantial number of numerical experiments are reported in Chapters 2, 3, 4 and 5, in which we compare the performance of our methods to state-of-the-art methods used to train DNNs, that demonstrate the efficiency and effectiveness of our proposed new second-order methods.