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Stochastic gradient Markov chain Monte Carlo (SG-MCMC) has been increasingly popular in Bayesian learning due to its ability to deal with large data.
Some studies in machine learning using the game of checkers
A. Kolmogoroff · 1931
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Equation of State Calculations by Fast Computing Machines
N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller · 1953
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Some methods of speeding up the convergence of iteration methods
B. T. Polyak · 1964
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Stochastic Differential Equations
B. Øksendal, editor · 1985
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Hybrid Monte Carlo
S. Duane, A. D. Kennedy, B. J. Pendleton, and D. Roweth · 1987
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The Fokker-Planck equation
H. Risken · 1989
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A Generalized Guided Monte-Carlo Algorithm
A. M. Horowitz · 1991
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The variational formulation of the Fokker-Planck equation
R. Jordan, D. Kinderlehrer, and F. Otto · 1998
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Dynamics of Labyrinthine pattern formation in magnetic fluids: A mean-field theory
F. Otto · 1998
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Optimal transport: old and new
C. Villani · 2008
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Bayesian learning via stochastic gradient Langevin dynamics
M. Welling and Y. W. Teh · 2011
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Backward and Forward Equations for Diffusion Processes
A. P. Ghosh · 2011
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Stochastic gradient Hamiltonian Monte Carlo
T. Chen, E. B. Fox, and C. Guestrin · 2014
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Bayesian sampling using stochastic gradient thermostats
N. Ding, Y. Fang, R. Babbush, C. Chen, R. D. Skeel, and H. Neven · 2014
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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(exploration of the (Non-)asymptotic bias and variance of stochastic gradient Langevin dynamics
S. J. Vollmer, K. C. Zygalakis, and Y. W. Teh · 2016
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Stein variational gradient descent: A general purpose Bayesian inference algorithm
Q. Liu and D. Wang · 2016
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Stein variational gradient descent: A general purpose bayesian inference algorithm
Qiang Liu and Dilin Wang · 2016
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Stein variational gradient descent as gradient flow
Qiang Liu · 2017
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Continuous-time flows for deep generative models
C. Chen, C. Li, L. Chen, W. Wang, Y. Pu, and L. Carin · 2017
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Towards principled methods for training generative adversarial networks
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On the convergence of stochastic gradient MCMC algorithms with high-order integrators
C. Chen, N. Ding, and L. Carin · 2015
Cited alongside, same era.
Consistency and fluctuations for stochastic gradient Langevin dynamics
Y. W. Teh, A. H. Thiery, and S. J. Vollmer · 2016
Cited alongside, same era.
M. Arjovsky and L. Bottou · 2017
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M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Understanding GANs: the LQG setting
S. Feizi, C. Suh, F. Xia, and D. Tse · 2017
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