Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels | Paper Citation Record

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This paper cites A tutorial on energy-based learning.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels A tutorial on energy-based learning

Reference 1

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This paper cites Noise-contrastive estimation: A new estimation principle for unnormalized statistical models.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Noise-contrastive estimation: A new estimation principle for unnormalized statistical models

Reference 2

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This paper cites Sliced score matching: A scalable approach to density and score estimation.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Sliced score matching: A scalable approach to density and score estimation

Reference 3

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This paper cites A survey on bias and fairness in machine learning.ACM Computing Surveys (CSUR), 54(6):1–35, 2021.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels A survey on bias and fairness in machine learning.ACM Computing Surveys (CSUR), 54(6):1–35, 2021

Reference 4

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This paper cites Estimation of non-normalized statistical models by score matching.Journal of Machine Learning Research, 6(4), 2005.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Estimation of non-normalized statistical models by score matching.Journal of Machine Learning Research, 6(4), 2005

Reference 5

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This paper cites Training products of experts by minimizing contrastive divergence.Neural computation, 14(8):1771–1800, 2002.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Training products of experts by minimizing contrastive divergence.Neural computation, 14(8):1771–1800, 2002

Reference 6

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This paper cites A new learning algorithm for mean field boltzmann machines.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels A new learning algorithm for mean field boltzmann machines

Reference 7

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This paper cites On contrastive divergence learning.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels On contrastive divergence learning

Reference 8

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This paper cites Connections between score matching, contrastive divergence, and pseudo- likelihood for continuous-valued variables.IEEE Transactions on neural networks, 18(5): 1529–1531, 2007.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Connections between score matching, contrastive divergence, and pseudo- likelihood for continuous-valued variables.IEEE Transactions on neural networks, 18(5): 1529–1531, 2007

Reference 9

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This paper cites Efficient training of energy-based models using jarzynski equality.Advances in Neural Information Processing Systems, 36, 2024.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Efficient training of energy-based models using jarzynski equality.Advances in Neural Information Processing Systems, 36, 2024

Reference 10

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This paper cites Generative models as out-of-equilibrium particle systems: Training of energy-based models using non-equilibrium thermodynamics.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Generative models as out-of-equilibrium particle systems: Training of energy-based models using non-equilibrium thermodynamics

Reference 11

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This paper cites How to Train Your Energy-Based Models.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels How to Train Your Energy-Based Models

Reference 12

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This paper cites A theory of generative convnet.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels A theory of generative convnet

Reference 13

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This paper cites Your classifier is secretly an energy based model and you should treat it like one.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Your classifier is secretly an energy based model and you should treat it like one

Reference 14

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This paper cites CRC press, 2011.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels CRC press, 2011

Reference 15

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This paper cites Springer, 2001.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Springer, 2001

Reference 16

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This paper cites A connection between score matching and denoising autoencoders.Neural computation, 23(7):1661–1674, 2011.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels A connection between score matching and denoising autoencoders.Neural computation, 23(7):1661–1674, 2011

Reference 17

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This paper cites On autoencoders and score matching for energy based models.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels On autoencoders and score matching for energy based models

Reference 18

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This paper cites On the failure of variational score matching for VAE models.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels On the failure of variational score matching for VAE models

Reference 19

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This paper cites Learning deep kernels for exponential family densities.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Learning deep kernels for exponential family densities

Reference 20

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This paper cites Generative modeling by estimating gradients of the data distribution.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Generative modeling by estimating gradients of the data distribution

Reference 21

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This paper cites Training restricted boltzmann machines using approximations to the likeli- hood gradient.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Training restricted boltzmann machines using approximations to the likeli- hood gradient

Reference 22

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This paper cites Cooperative training of descriptor and generator networks.IEEE transactions on pattern analysis and machine intelligence, 42(1):27–45, 2018.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Cooperative training of descriptor and generator networks.IEEE transactions on pattern analysis and machine intelligence, 42(1):27–45, 2018

Reference 23

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This paper cites Learning non-convergent non- persistent short-run MCMC toward energy-based model.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Learning non-convergent non- persistent short-run MCMC toward energy-based model

Reference 24

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This paper cites Flow contrastive estimation of energy-based models.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Flow contrastive estimation of energy-based models

Reference 25

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This paper cites Nonequilibrium equality for free energy differences.Physical Review Letters, 78 (14):2690, 1997.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Nonequilibrium equality for free energy differences.Physical Review Letters, 78 (14):2690, 1997

Reference 26

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This paper cites Annealed importance sampling.Statistics and computing, 11:125–139, 2001.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Annealed importance sampling.Statistics and computing, 11:125–139, 2001

Reference 27

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This paper cites Springer, 2001.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Springer, 2001

Reference 28

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This paper cites Auto-encoding sequential monte carlo.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Auto-encoding sequential monte carlo

Reference 29

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This paper cites Freedman.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Freedman

Reference 30

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Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Unresolved cited work

Reference 31

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This paper cites Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and mcmc.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and mcmc

Reference 32

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This paper cites Deep unsu- pervised learning using nonequilibrium thermodynamics.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Deep unsu- pervised learning using nonequilibrium thermodynamics

Reference 33

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This paper cites Deep boltzmann machines.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Deep boltzmann machines

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This paper cites A practical guide to training restricted boltzmann machines.Neural Networks: Tricks of the Trade: Second Edition, pages 599–619, 2012.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels A practical guide to training restricted boltzmann machines.Neural Networks: Tricks of the Trade: Second Edition, pages 599–619, 2012

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Observation f0fecbd4-1f4d-4d5c-a363-da34e8d72818 · outbound

This paper cites Improved learning of gaussian-bernoulli restricted boltzmann machines.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Improved learning of gaussian-bernoulli restricted boltzmann machines

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This paper cites Flow matching for generative modeling.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Flow matching for generative modeling

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This paper cites Stochastic Interpolants: A Unifying Framework for Flows and Diffusions.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

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This paper cites Springer-Verlag Berlin Heidelberg, 6 edition, 2003.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Springer-Verlag Berlin Heidelberg, 6 edition, 2003

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Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Unresolved cited work

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Observation 5161117d-e01e-4b3c-a42c-cf5ab3491527 · outbound

This paper cites Expansion of the global error for numerical schemes solving stochastic differential equations.Stochastic Analysis and Applications, 8(4):483–509, 1990.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Expansion of the global error for numerical schemes solving stochastic differential equations.Stochastic Analysis and Applications, 8(4):483–509, 1990

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Observation e6939399-7397-4edf-9798-f8abd66cc324 · outbound

This paper cites Generative models as out-of-equilibrium particle systems: the case of energy- based models.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Generative models as out-of-equilibrium particle systems: the case of energy- based models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:37:34.515926Z

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Observation 00f60c19-1d5a-4b13-9c19-9df96e8ad6e3 · outbound

This paper cites Correlation functions and computer simulations.Nuclear Physics B, 180(3): 378–384, 1981.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Correlation functions and computer simulations.Nuclear Physics B, 180(3): 378–384, 1981

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:37:34.504289Z

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Observation e6be829b-66df-4fb2-80b3-4bc4cf298589 · outbound

This paper cites Nonasymptotic convergence analysis for the unadjusted langevin algorithm.THE ANNALS of APPLIED PROBABILITY, pages 1551–1587, 2017.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Nonasymptotic convergence analysis for the unadjusted langevin algorithm.THE ANNALS of APPLIED PROBABILITY, pages 1551–1587, 2017

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:37:34.492497Z

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Observation 8eced11c-def4-4611-87ce-c36714cbfbdb · outbound

This paper cites Escorted free energy simulations: Improving convergence by reducing dissipation.Physical Review Letters, 100(19):190601, 2008.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Escorted free energy simulations: Improving convergence by reducing dissipation.Physical Review Letters, 100(19):190601, 2008

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Observation 70807f41-0f9b-49df-8847-ce375a67b57c · outbound

This paper cites A learning algorithm for boltzmann machines.Cognitive science, 9(1):147–169, 1985.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels A learning algorithm for boltzmann machines.Cognitive science, 9(1):147–169, 1985

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Observation e168b2c1-e427-42a9-94dd-7a284199ed8b · outbound

This paper cites An introduction to restricted boltzmann machines.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels An introduction to restricted boltzmann machines

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Observation 33d22df8-206c-48fb-8405-229cb84b82e6 · outbound

This paper cites An overview on restricted boltzmann machines.Neurocomputing, 275:1186–1199, 2018.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels An overview on restricted boltzmann machines.Neurocomputing, 275:1186–1199, 2018

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:37:34.456692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 01952a49-2ef3-4e5e-8db7-5429b8fb3bdb · outbound

This paper cites American Mathematical Society, 2022.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels American Mathematical Society, 2022

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T05:37:34.365179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 998db3c1-fcf1-48db-8eea-bbad28f80a33 · outbound

This paper cites Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks.

Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:37:34.405200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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