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Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks

Part of Advances in Neural Information Processing Systems 30 (NIPS 2017)

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Authors

Nanyang Ye, Zhanxing Zhu, Rafal Mantiuk

Abstract

Minimizing non-convex and high-dimensional objective functions is challenging, especially when training modern deep neural networks. In this paper, a novel approach is proposed which divides the training process into two consecutive phases to obtain better generalization performance: Bayesian sampling and stochastic optimization. The first phase is to explore the energy landscape and to capture the fatmod`temperature dynamics''. These strategies can overcome the challenge of early trapping into bad local minima and have achieved remarkable improvements in various types of neural networks as shown in our theoretical analysis and empirical experiments.