Preconditioned stochastic gradient langevin dynamics for deep neural networks

Published

Conference Paper

© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Effective training of deep neural networks suffers from two main issues. The first is that the parameter spaces of these models exhibit pathological curvature. Recent methods address this problem by using adaptive preconditioning for Stochastic Gradient Descent (SGD). These methods improve convergence by adapting to the local geometry of parameter space. A second issue is overfitting, which is typically addressed by early stopping. However, recent work has demonstrated that Bayesian model averaging mitigates this problem. The posterior can be sampled by using Stochastic Gradient Langevin Dynamics (SGLD). However, the rapidly changing curvature renders default SGLD methods inefficient. Here, we propose combining adaptive preconditioners with SGLD. In support of this idea, we give theoretical properties on asymptotic convergence and predictive risk.We also provide empirical results for Logistic Regression, Feedforward Neural Nets, and Convolutional Neural Nets, demonstrating that our preconditioned SGLD method gives state-of-the-art performance on these models.

Duke Authors

Cited Authors

  • Li, C; Chen, C; Carlson, D; Carin, L

Published Date

  • January 1, 2016

Published In

  • 30th Aaai Conference on Artificial Intelligence, Aaai 2016

Start / End Page

  • 1788 - 1794

International Standard Book Number 13 (ISBN-13)

  • 9781577357605

Citation Source

  • Scopus