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Boltzmann generators approach the sampling problem in many-body physics by combining a normalizing flow and a statistical reweighting method to generate samples of a physical system's equilibrium density.
Replica monte carlo simulation of spin-glasses
Robert H Swendsen and Jian-Sheng Wang · 1986
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Hybrid monte carlo
Simon Duane, Anthony D Kennedy, Brian J Pendleton, and Duncan Roweth · 1987
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Markov chain monte carlo maximum likelihood
Charles J. Geyer · 1991
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Exchange monte carlo method and application to spin glass simulations
K. Hukushima and K. Nemoto · 1996
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Density estimation by dual ascent of the log-likelihood
Esteban G. Tabak and Eric Vanden-Eijnden · 2010
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Nice: Nonlinear independent components estimation
L. Dinh, D. Krueger, and Y. Bengio · 2015
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Variational inference with normalizing flows
D. J. Rezende and S. Mohamed · 2015
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Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Neural network renormalization group
S. Li and Lei Wang · 2018
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Monge-Ampère flow for generative modeling
Linfeng Zhang, Lei Wang, et al · 2018
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Flow-based generative models for markov chain monte carlo in lattice field theory
MS Albergo, G Kanwar, and PE Shanahan · 2019
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Hamiltonian neural networks
Samuel Greydanus, Misko Dzamba, and Jason Yosinski · 2019
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Boltzmann generators-sampling equilibrium states of many-body systems with deep learning
Frank Noé, Simon Olsson, Jonas Köhler, and Hao Wu · 2019
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Sampling using SU(N) gauge equivariant flows
D. Boyda, G. Kanwar, Sébastien Racanière, Danilo Jimenez Rezende, M. S. Albergo, K. Cranmer, D. Hackett, and P. Shanahan · 2020
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Augmented normalizing flows: Bridging the gap between generative flows and latent variable models
C. Huang, Laurent Dinh, and Aaron C. Courville · 2020
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Normalizing flows: An introduction and review of current methods
Ivan Kobyzev, Simon Prince, and Marcus Brubaker · 2020
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Equivariant flows: exact likelihood generative learning for symmetric densities
Jonas Köhler, Leon Klein, and F. Noé · 2020
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Asymptotically unbiased estimation of physical observables with neural samplers
Kim A. Nicoli, Shinichi Nakajima, Nils Strodthoff, W. Samek, K. Müller, and P. Kessel · 2020
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George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2019
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Danilo Jimenez Rezende, Sébastien Racanière, Irina Higgins, and Peter Toth · 2019
Cited alongside, same era.
Hamiltonian generative networks
Peter Toth, Danilo Jimenez Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, and Irina Higgins · 2019
Cited alongside, same era.
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Disentanglement by nonlinear ICA with general incompressible-flow networks (GIN)
P. Sorrenson, C. Rother, and U. Köthe · 2020
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H. Wu, Jonas Köhler, and F. Noé · 2020
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