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arXiv · 1902.03932

Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning

Ruqi Zhang·Chunyuan Li·Jianyi Zhang·Changyou Chen·Andrew Gordon Wilson
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Abstract

The posteriors over neural network weights are high dimensional and multimodal. Each mode typically characterizes a meaningfully different representation of the data. We develop Cyclical Stochastic Gradient MCMC (SG-MCMC) to automatically explore such distributions. In particular, we propose a cyclical stepsize schedule, where larger steps discover new modes, and smaller steps characterize each mode. We also prove non-asymptotic convergence of our proposed algorithm. Moreover, we provide extensive experimental results, including ImageNet, to demonstrate the scalability and effectiveness of cyclical SG-MCMC in learning complex multimodal distributions, especially for fully Bayesian inference with modern deep neural networks.

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BibTeXRIS

Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson. 2019-02-11. Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning. https://arxiv.org/abs/1902.03932

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