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Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks

, , and . (2021)cite arxiv:2101.03906.

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Wormhole Hamiltonian Monte Carlo., , and . AAAI, page 1953-1959. AAAI Press, (2014)Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks, , and . (2021)cite arxiv:2101.03906.Modeling Binary Time Series Using Gaussian Processes with Application to Predicting Sleep States., , and . J. Classif., 35 (3): 549-579 (2018)Spherical Hamiltonian Monte Carlo for Constrained Target Distributions., , and . ICML, volume 32 of JMLR Workshop and Conference Proceedings, page 629-637. JMLR.org, (2014)Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes., , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 34409-34430. PMLR, (2023)Distributed Stochastic Gradient MCMC., , and . ICML, volume 32 of JMLR Workshop and Conference Proceedings, page 1044-1052. JMLR.org, (2014)Nonlinear Models using Dirichlet Process Mixtures, and . 0707. Department of Statistics, University of Toronto, (2007)A modified Dirichlet process mixture model for clustering phosphopeptides based on their response to anti-cancer drug perturbation., , , and . BIOCOMP, page 17-22. CSREA Press, (2008)Split Hamiltonian Monte Carlo., , , and . Stat. Comput., 24 (3): 339-349 (2014)Nonlinear Models Using Dirichlet Process Mixtures., and . J. Mach. Learn. Res., (2009)