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Recent research has focused on designing neural samplers that amortize the process of sampling from unnormalized densities.
Equation of state calculations by fast computing machines
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Monte Carlo sampling methods using Markov chains and their applications
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Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
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Replica Monte Carlo simulation of spin-glasses
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Hybrid Monte Carlo
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A stochastic estimator of the trace of the influence matrix for Laplacian smoothing splines
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Markov chain Monte Carlo maximum likelihood
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Probabilistic inference using Markov chain Monte Carlo methods
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Representations of knowledge in complex systems
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Exchange Monte Carlo method and application to spin glass simulations
Hukushima, K. and Nemoto, K · 1996
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Exponential convergence of langevin distributions and their discrete approximations
Roberts, G. O. and Tweedie, R. L · 1996
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Sequential Monte Carlo methods for dynamic systems
Liu, J. S. and Chen, R · 1998
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Annealed importance sampling
Neal, R. M · 2001
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Parallel tempering: Theory, applications, and new perspectives
Earl, D. J. and Deem, M. W · 2005
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Truncated importance sampling
Ionides, E. L · 2008
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Conditions for rapid mixing of parallel and simulated tempering on multimodal distributions
Woodard, D. B., Schmidler, S. C., and Huber, M · 2009
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Tweedie’s formula and selection bias
Efron, B · 2011
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A connection between score matching and denoising autoencoders
Vincent, P · 2011
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Bayesian inference in physics
Von Toussaint, U · 2011
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Bayesian learning via stochastic gradient Langevin dynamics
Welling, M. and Teh, Y. W · 2011
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Bayesian Data Analysis
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B · 2013
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., and Ganguli, S · 2015
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Schaul, T., Quan, J., Antonoglou, I., and Silver, D · 2016
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Neural ordinary differential equations
Chen, R. T., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K · 2018
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FFJORD: Free-form continuous dynamics for scalable reversible generative models
Grathwohl, W., Chen, R. T., Bettencourt, J., Sutskever, I., and Duvenaud, D · 2018
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Noé, F., Olsson, S., Köhler, J., and Wu, H · 2019
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Equivariant flows: exact likelihood generative learning for symmetric densities
Pseudoinverse-guided diffusion models for inverse problems
Song, J., Vahdat, A., Mardani, M., and Kautz, J · 2023
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Vargas, F., Grathwohl, W., and Doucet, A · 2023
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Path integral sampler: A stochastic control approach for sampling
Zhang, Q. and Chen, Y · 2023
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Iterated denoising energy matching for sampling from boltzmann densities
Akhound-Sadegh, T., Rector-Brooks, J., Bose, J., Mittal, S., Lemos, P., Liu, C.-H., Sendera, M., Ravanbakhsh, S., Gidel, G., Bengio, Y., et al · 2024
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Nets: A non-equilibrium transport sampler
Albergo, M. S. and Vanden-Eijnden, E · 2024
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Köhler, J., Klein, L., and Noé, F · 2020
Cited alongside, same era.
Stochastic normalizing flows
Wu, H., Köhler, J., and Noe, F · 2020
Cited alongside, same era.
Annealed flow transport monte carlo
Arbel, M., Matthews, A., and Doucet, A · 2021
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Pot: Python optimal transport
Flamary, R., Courty, N., Gramfort, A., Alaya, M. Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N. T., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D. J., Tavenard, R., Tong, A., and Vayer, T · 2021
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Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2021
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E(n) equivariant graph neural networks
Satorras, V. G., Hoogeboom, E., and Welling, M · 2021
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2021
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Beyond ELBOs: A large-scale evaluation of variational methods for sampling
Blessing, D., Jia, X., Esslinger, J., Vargas, F., and Neumann, G · 2024
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De Bortoli, V., Hutchinson, M., Wirnsberger, P., and Doucet, A · 2024
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Training neural samplers with reverse diffusive KL divergence
He, J., Chen, W., Zhang, M., Barber, D., and Hernández-Lobato, J. M · 2024
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Guiding a diffusion model with a bad version of itself
Karras, T., Aittala, M., Kynkäänniemi, T., Lehtinen, J., Aila, T., and Laine, S · 2024
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Equivariant flow matching
Klein, L., Krämer, A., and Noé, F · 2024
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Diff-instruct: A universal approach for transferring knowledge from pre-trained diffusion models
Luo, W., Hu, T., Zhang, S., Sun, J., Li, Z., and Zhang, Z · 2024
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Computing hydration free energies of small molecules with first principles accuracy
Moore, J. H., Cole, D. J., and Csanyi, G · 2024
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Bnem: A boltzmann sampler based on bootstrapped noised energy matching
OuYang, R., Qiang, B., and Hernández-Lobato, J. M · 2024
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Particle denoising diffusion sampler
Phillips, A., Dau, H.-D., Hutchinson, M. J., De Bortoli, V., Deligiannidis, G., and Doucet, A · 2024
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Free hunch: Denoiser covariance estimation for diffusion models without extra costs
Rissanen, S., Heinonen, M., and Solin, A · 2024
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Improved off-policy training of diffusion samplers
Sendera, M., Kim, M., Mittal, S., Lemos, P., Scimeca, L., Rector-Brooks, J., Adam, A., Bengio, Y., and Malkin, N · 2024
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Uniform ergodicity of parallel tempering with efficient local exploration
Surjanovic, N., Syed, S., Bouchard-Côté, A., and Campbell, T · 2024
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Transport meets variational inference: Controlled Monte Carlo diffusions
Vargas, F., Padhy, S., Blessing, D., and Nüsken, N · 2024
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Adjoint sampling: Highly scalable diffusion samplers via adjoint matching
Havens, A., Miller, B. K., Yan, B., Domingo-Enrich, C., Sriram, A., Wood, B., Levine, D., Hu, B., Amos, B., Karrer, B., et al · 2025
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No trick, no treat: Pursuits and challenges towards simulation-free training of neural samplers
He, J., Du, Y., Vargas, F., Zhang, D., Padhy, S., OuYang, R., Gomes, C., and Hernández-Lobato, J. M · 2025
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Thermodynamic interpolation: A generative approach to molecular thermodynamics and kinetics
Moqvist, S., Chen, W., Schreiner, M., Nüske, F., and Olsson, S · 2025
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Feynman-Kac correctors in diffusion: Annealing, guidance, and product of experts
Skreta, M., Akhound-Sadegh, T., Ohanesian, V., Bondesan, R., Aspuru-Guzik, A., Doucet, A., Brekelmans, R., Tong, A., and Neklyudov, K · 2025
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Generalised parallel tempering: Flexible replica exchange via flows and diffusions
Zhang, L., Potaptchik, P., Doucet, A., Dau, H.-D., and Syed, S · 2025
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