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We tackle the problem of sampling from intractable high-dimensional density functions, a fundamental task that often appears in machine learning and statistics.
Hybrid monte carlo
Simon Duane, Anthony D. Kennedy, Brian Pendleton, and Duncan Roweth · 1987
Earlier work this paper cites.
Bayesian learning for neural networks
Radford M. Neal · 1995
Earlier work this paper cites.
Understanding molecular simulation: from algorithms to applications
Daan Frenkel and Berend Smit · 1996
Earlier work this paper cites.
Log gaussian cox processes
Jesper M. Møller, Anne Randi Syversveen, and Rasmus Waagepetersen · 1998
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Annealed importance sampling
Radford M. Neal · 1998
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Bias-variance error bounds for temporal difference updates
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Sequential monte carlo methods in practice
A. Doucet, Nando de Freitas, and Neil J. Gordon · 2001
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Monte carlo strategies in scientific computing
Jun S. Liu · 2001
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Expectation propagation for approximate Bayesian inference
Thomas P. Minka · 2001
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Sequential monte carlo samplers
Pierre Del Moral and A. Doucet · 2002
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Slice sampling
Radford M. Neal · 2003
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An introduction to mcmc for machine learning
Christophe Andrieu, Nando de Freitas, A. Doucet, and Michael I. Jordan · 2004
Earlier work this paper cites.
Path integrals and symmetry breaking for optimal control theory
Hilbert J Kappen · 2005
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and P. Abbeel · 2006
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Using fast weights to improve persistent contrastive divergence
Tijmen Tieleman and Geoffrey E. Hinton · 2009
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Tempered markov chain monte carlo for training of restricted boltzmann machines
Guillaume Desjardins, Aaron C. Courville, Yoshua Bengio, Pascal Vincent, and Olivier Delalleau · 2010
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Handbook of markov chain monte carlo
Steve P. Brooks, Andrew Gelman, Galin L. Jones, and Xiao-Li Meng · 2011
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The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo
Matthew D. Hoffman and Andrew Gelman · 2011
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A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee Whye Teh · 2011
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Stochastic gradient hamiltonian monte carlo
Tianqi Chen, Emily B. Fox, and Carlos Guestrin · 2014
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Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Auto-encoding variational Bayes
Diederik P. Kingma and Max Welling · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Probabilistic backpropagation for scalable learning of bayesian neural networks
José Miguel Hernández-Lobato and Ryan P. Adams · 2015
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Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Narain Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Variational inference: A review for statisticians
David M. Blei, Alp Kucukelbir, and Jon D. McAuliffe · 2016
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Importance weighted autoencoders
Yuri Burda, Roger Baker Grosse, and Ruslan Salakhutdinov · 2016
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Sticking the landing: Simple, lower-variance gradient estimators for variational inference
Geoffrey Roeder, Yuhuai Wu, and David Kristjanson Duvenaud · 2017
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Neural ordinary differential equations
Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David Kristjanson Duvenaud · 2018
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Molecular dynamics simulation for all
Scott A. Hollingsworth and Ron O. Dror · 2018
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Neural importance sampling
Thomas Müller, Brian McWilliams, Fabrice Rousselle, Markus H. Gross, and Jan Novák · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Bayesian structure learning with generative flow networks
Tristan Deleu, António Góis, Chris Emezue, Mansi Rankawat, Simon Lacoste-Julien, Stefan Bauer, and Yoshua Bengio · 2022
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Score-based diffusion meets annealed importance sampling
A. Doucet, Will Grathwohl, Alexander G. de G. Matthews, and Heiko Strathmann · 2022
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Weitao Du, Tao Yang, Heidi Zhang, and Yuanqi Du · 2022
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Langevin diffusion variational inference
Tomas Geffner and Justin Domke · 2022
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Stochastic optimal control for collective variable free sampling of molecular transition paths
Lars Holdijk, Yuanqi Du, Ferry Hooft, Priyank Jaini, Bernd Ensing, and Max Welling · 2022
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Solving statistical mechanics using variational autoregressive networks
Dian Wu, Lei Wang, and Pan Zhang · 2018
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Flow-based generative models for markov chain monte carlo in lattice field theory
Michael S Albergo, Gurtej Kanwar, and Phiala E. Shanahan · 2019
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Frank Noé, Jonas Köhler, and Hao Wu · 2019
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Theoretical guarantees for sampling and inference in generative models with latent diffusions
Belinda Tzen and Maxim Raginsky · 2019
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Neural approximate sufficient statistics for implicit models
Yanzhi Chen, Dinghuai Zhang, Michael U Gutmann, Aaron C. Courville, and Zhanxing Zhu · 2020
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i-flow: High-dimensional integration and sampling with normalizing flows
Christina Gao, Joshua Isaacson, and Claudius Krause · 2020
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Biological sequence design with GFlowNets
Moksh Jain, Emmanuel Bengio, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Bonaventure F.P. Dossou, Chanakya Ekbote, Jie Fu, Tianyu Zhang, Micheal Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, and Yoshua Bengio · 2022
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Convergence of score-based generative modeling for general data distributions
Holden Lee, Jianfeng Lu, and Yixin Tan · 2022
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Gflowout: Dropout with generative flow networks
Dianbo Liu, Moksh Jain, Bonaventure F. P. Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal, Nikolay Malkin, Chris C. Emezue, Dinghuai Zhang, Nadhir Hassen, Xu Ji, Kenji Kawaguchi, and Yoshua Bengio · 2022
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Learning GFlowNets from partial episodes for improved convergence and stability
Kanika Madan, Jarrid Rector-Brooks, Maksym Korablyov, Emmanuel Bengio, Moksh Jain, Andrei Nica, Tom Bosc, Yoshua Bengio, and Nikolay Malkin · 2022
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Flow annealed importance sampling bootstrap
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Infinitely deep bayesian neural networks with stochastic differential equations
Winnie Xu, Ricky T. Q. Chen, Xuechen Li, and David Duvenaud · 2022
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Path integral sampler: a stochastic control approach for sampling
Qinsheng Zhang and Yongxin Chen · 2022
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A variational perspective on generative flow networks
Heiko Zimmermann, Fredrik Lindsten, J.-W. van de Meent, and Christian Andersson Naesseth · 2022
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Dyngfn: Bayesian dynamic causal discovery using generative flow networks
Lazar Atanackovic, Alexander Tong, Jason S. Hartford, Leo J. Lee, Bo Wang, and Yoshua Bengio · 2023
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GFlowNet foundations
Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J Hu, Mo Tiwari, and Emmanuel Bengio · 2023
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Joint Bayesian inference of graphical structure and parameters with a single generative flow network
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Zhenggqi Gao, Dinghuai Zhang, Luca Daniel, and Duane S. Boning · 2023
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Multi-fidelity active learning with gflownets
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GFlowNet-EM for learning compositional latent variable models
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Reverse diffusion monte carlo
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A theory of continuous generative flow networks
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GFlowNets and variational inference
Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward Hu, Katie Everett, Dinghuai Zhang, and Yoshua Bengio · 2023
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Detecting and mitigating mode-collapse for flow-based sampling of lattice field theories
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Improved sampling via learned diffusions
Lorenz Richter, Julius Berner, and Guan-Horng Liu · 2023
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