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Evaluating Approximate Inference in Bayesian Deep Learning.

, , , , , , , , , and . NeurIPS (Competition and Demos), volume 176 of Proceedings of Machine Learning Research, page 113-124. PMLR, (2021)

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Generalised Wishart Processes., and . UAI, page 736-744. AUAI Press, (2011)Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning., , , , and . ICLR, OpenReview.net, (2020)Deep Kernel Learning, , , and . (2015)cite arxiv:1511.02222Comment: 19 pages, 6 figures.Deep Kernel Learning., , , and . AISTATS, volume 51 of JMLR Workshop and Conference Proceedings, page 370-378. JMLR.org, (2016)SWALP : Stochastic Weight Averaging in Low Precision Training., , , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 7015-7024. PMLR, (2019)Modelling Input Varying Correlations between Multiple Responses., and . ECML/PKDD (2), volume 7524 of Lecture Notes in Computer Science, page 858-861. Springer, (2012)Should We Learn Most Likely Functions or Parameters?, , , and . CoRR, (2023)Large Language Models Are Zero-Shot Time Series Forecasters., , , and . CoRR, (2023)Scalable Levy Process Priors for Spectral Kernel Learning., , , and . NIPS, page 3940-3949. (2017)Bayesian GAN., and . NIPS, page 3622-3631. (2017)