Cyclical Stochastic Gradient MCMC for Bayesian Deep LearningDownload PDF

25 Sept 2019, 19:19 (modified: 23 Jan 2023, 18:06)ICLR 2020 Conference Blind SubmissionReaders: Everyone
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Code: [![github](/images/github_icon.svg) ruqizhang/csgmcmc](https://github.com/ruqizhang/csgmcmc) + [![Papers with Code](/images/pwc_icon.svg) 3 community implementations](https://paperswithcode.com/paper/?openreview=rkeS1RVtPS)
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 prove non-asymptotic convergence theory of our proposed algorithm. Moreover, we provide extensive experimental results, including ImageNet, to demonstrate the effectiveness of cyclical SG-MCMC in learning complex multimodal distributions, especially for fully Bayesian inference with modern deep neural networks.
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