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Generating a Boltzmann distribution in high dimension has recently been achieved with Normalizing Flows, which enable fast and exact computation of the generated density, and thus unbiased estimation of expectations.
Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, and Kevin Swersky · 1905
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Normalizing flows for probabilistic modeling and inference, 2019
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 1912
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Replica monte carlo simulation of spin-glasses
Robert Swendsen and Jian-Sheng Wang · 1986
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Replica-exchange molecular dynamics method for protein folding
Yuji Sugita and Yuko Okamoto · 1999
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Chin-Wei Huang, Laurent Dinh, and Aaron Courville · 2002
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Stochastic normalizing flows, 2020
Hao Wu, Jonas Köhler, and Frank Noé · 2002
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Parallel tempering: Theory, applications, and new perspectives
David J. Earl and Michael W. Deem · 2005
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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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Training invertible linear layers through rank-one perturbations, 2020
Andreas Krämer, Jonas Köhler, and Frank Noé · 2010
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Machine learning : a probabilistic perspective
Kevin P. Murphy · 2012
Cited alongside, same era.
Temperature-steerable flows, 2020
Manuel Dibak, Leon Klein, and Frank Noé · 2012
Cited alongside, same era.
A family of nonparametric density estimation algorithms
E. Tabak and Turner Cristina · 2013
Cited alongside, same era.
Variational inference with normalizing flows, 2015
Danilo Jimenez Rezende and Shakir Mohamed · 2015
Cited alongside, same era.
Density estimation using real nvp, 2016
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
Cited alongside, same era.
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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Vargrad: A low-variance gradient estimator for variational inference
Lorenz Richter, Ayman Boustati, Nikolas Nüsken, Francisco J. R. Ruiz, and Ömer Deniz Akyildiz · 2020
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BackPACK: Packing more into backprop
Felix Dangel, Frederik Kunstner, and Philipp Hennig · 2020
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Smooth normalizing flows, 2021
Jonas Köhler, Andreas Krämer, and Frank Noé · 2021
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Resampling base distributions of normalizing flows, 2021
Vincent Stimper, Bernhard Schölkopf, and José Miguel Hernández-Lobato · 2021
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CHARMM36m: an improved force field for folded and intrinsically disordered proteins
Jing Huang, Sarah Rauscher, Grzegorz Nawrocki, Ting Ran, Michael Feig, Bert L de Groot, Helmut Grubmüller, and Alexander D MacKerell · 2016
Cited alongside, same era.
Continuously differentiable exponential linear units
Jonathan T. Barron · 2017
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Graph attention networks, 2017
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions, 2017
Kristof T. Schütt, Pieter-Jan Kindermans, Huziel E. Sauceda, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
Cited alongside, same era.
Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, and José Miguel Hernández-Lobato · 2021
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Temperature steerable flows and boltzmann generators, 2021
Manuel Dibak, Leon Klein, and Frank Noé · 2021
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Enhanced sampling methods for molecular dynamics simulations, 2022
Jérôme Hénin, Tony Lelièvre, Michael R. Shirts, Omar Valsson, and Lucie Delemotte · 2022
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Adaptive monte carlo augmented with normalizing flows
Marylou Gabrié, Grant M. Rotskoff, and Eric Vanden-Eijnden · 2022
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