Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks
This addresses the problem of non-convex optimization in deep learning for researchers and practitioners, offering an incremental method to enhance training efficiency and performance.
The paper tackles the challenge of training deep neural networks by proposing a two-phase approach combining Bayesian sampling with stochastic optimization to improve generalization, achieving notable improvements across various network types.
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 "fat" modes; and the second one is to fine-tune the parameter learned from the first phase. In the Bayesian learning phase, we apply continuous tempering and stochastic approximation into the Langevin dynamics to create an efficient and effective sampler, in which the temperature is adjusted automatically according to the designed "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.