Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
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.
Code: [ ruqizhang/csgmcmc](https://github.com/ruqizhang/csgmcmc) + [ 3 community implementations](https://paperswithcode.com/paper/?openreview=rkeS1RVtPS)
Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/cyclical-stochastic-gradient-mcmc-for/code)
Original Pdf: pdf
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