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Link to paper: https://openreview.net/forum?id=Rprd8... References: Betancourt, Michael. "The fundamental incompatibility of scalable Hamiltonian Monte Carlo and naive data subsampling." International Conference on Machine Learning. 2015.Giovanni Bussi and Michele Parrinello. Accurate sampling using Langevin dynamics. Physical Review E, 75, 2007. https://doi.org/10.1103/PhysRevE.75.0... Chen, Emily Fox, and Carlos Guestrin. Stochastic gradient Hamiltonian Monte Carlo. In Proceedings of the 31st International Conference on Machine Learning (ICML 2014). http://proceedings.mlr.press/v32/chen... Duane, A.D. Kennedy, Brian J. Pendleton, and Duncan Roweth. Hybrid monte carlo. Physics Letters B, 195(2):216 – 222, 1987. https://doi.org/10.1016/0370-2693(87).... Hastings. Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1):97–109, 1970.Alan M. Horowitz. A generalized guided Monte Carlo algorithm. Physics Letters B, 268(2): 247 – 252, 1991. doi: https://doi.org/10.1016/0370-2693(91)... Leimkuhler and Charles Matthews. Numerical Methods for Stochastic Molecular Dy- namics, chapter 7, pages 261–328. URL https://doi.org/10.1007/ 978-3-319-16375-8_7.Nicholas Metropolis, Arianna W Rosenbluth, Marshall N Rosenbluth, Augusta H Teller, and Edward Teller. Equation of state calculations by fast computing machines. The journal of chemical physics, 21(6):1087–1092, 1953.Radford M. Neal. Bayesian learning for neural networks, volume 118. Springer, 1996Radford M. Neal. MCMC using Hamiltonian dynamics. 2012. URL http://arxiv.org/abs/1206.1901v1 Max Welling and Yee Whye Teh. Bayesian learning via stochastic gradient Langevin dynamics. In Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pages 681–688, 2011.Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Świątkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, and Sebastian Nowozin. How good is the Bayes posterior in deep neural networks really?, 2020. URL http://arxiv. org/abs/2002.02405v1.Ruqi Zhang, A. Feder Cooper, and Christopher De Sa. AMAGOLD: Amortized Metropolis adjustment for efficient stochastic gradient MCMC. 2020. URL http://arxiv.org/abs/2003.00193v1.