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Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem in science.
Neural stochastic differential equations: Deep latent Gaussian models in the diffusion limit
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Equation of state calculations by fast computing machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E · 1953
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Monte carlo sampling methods using markov chains and their applications
Hastings, W. K · 1970
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Optimization by simulated annealing
Kirkpatrick, S., Gelatt Jr, C. D., and Vecchi, M. P · 1983
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Stochastic control and nonequilibrium thermodynamical systems
Pavon, M · 1989
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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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Optimal scaling of discrete approximations to langevin diffusions
Roberts, G. O. and Rosenthal, J. S · 1998
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Monte Carlo statistical methods , volume 2
Robert, C. P., Casella, G., and Casella, G · 1999
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Annealed importance sampling
Neal, R. M · 2001
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Slice sampling
Neal, R. M · 2003
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Sequential Monte Carlo samplers
Del Moral, P., Doucet, A., and Jasra, A · 2006
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Nested sampling for general Bayesian computation
Skilling, J · 2006
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Training restricted boltzmann machines using approximations to the likelihood gradient
Tieleman, T · 2008
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Graphical models, exponential families, and variational inference
Wainwright, M. J., Jordan, M. I., et al · 2008
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MCMC using Hamiltonian dynamics
Neal, R. M. et al · 2011
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Importance nested sampling and the MultiNest algorithm
Feroz, F., Hobson, M. P., Cameron, E., and Pettitt, A. N · 2013
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Rational construction of stochastic numerical methods for molecular sampling
Leimkuhler, B. and Matthews, C · 2013
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Monte Carlo theory, methods and examples
Owen, A. B · 2013
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The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo
Hoffman, M. D., Gelman, A., et al · 2014
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Polychord: nested sampling for cosmology
Handley, W., Hobson, M., and Lasenby, A · 2015
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Variational inference with normalizing flows
Rezende, D. and Mohamed, S · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Adaptive importance sampling: The past, the present, and the future
Bugallo, M. F., Elvira, V., Martino, L., Luengo, D., Miguez, J., and Djuric, P. M · 2017
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Density estimation using Real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2017
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Neural ordinary differential equations
Chen, R. T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K · 2018
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Neural network renormalization group
Li, S.-H. and Wang, L · 2018
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High-dimensional probability: An introduction with applications in data science
Vershynin, R · 2018
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Flow-based generative models for markov chain monte carlo in lattice field theory
Albergo, M. S., Kanwar, G., and Shanahan, P. E · 2019
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Equivariant diffusion for molecule generation in 3d
Hoogeboom, E., Satorras, V. G., Vignac, C., and Welling, M · 2022
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Equivariant 3d-conditional diffusion models for molecular linker design
Igashov, I., Stärk, H., Vignac, C., Satorras, V. G., Frossard, P., Welling, M., Bronstein, M., and Correia, B · 2022
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Rectified flow: A marginal preserving approach to optimal transport
Liu, Q · 2022
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Continual repeated annealed flow transport monte carlo
Matthews, A., Arbel, M., Rezende, D. J., and Doucet, A · 2022
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Geodiff: A geometric diffusion model for molecular conformation generation
Xu, M., Yu, L., Song, Y., Shi, C., Ermon, S., and Tang, J · 2022
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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
Köhler, J., Klein, L., and Noé, F · 2020
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Asymptotically unbiased estimation of physical observables with neural samplers
Nicoli, K. A., Nakajima, S., Strodthoff, N., Samek, W., Müller, K.-R., and Kessel, P · 2020
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DYNESTY: a dynamic nested sampling package for estimating Bayesian posteriors and evidences
Speagle, J. S · 2020
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Stochastic normalizing flows
Wu, H., Köhler, J., and Noé, F · 2020
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Path integral sampler: a stochastic control approach for sampling
Zhang, Q. and Chen, Y · 2022
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Building normalizing flows with stochastic interpolants
Albergo, M. S. and Vanden-Eijnden, E · 2023
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Self-consuming generative models go mad
Alemohammad, S., Casco-Rodriguez, J., Luzi, L., Humayun, A. I., Babaei, H., LeJeune, D., Siahkoohi, A., and Baraniuk, R. G · 2023
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On the stability of iterative retraining of generative models on their own data
Bertrand, Q., Bose, A. J., Duplessis, A., Jiralerspong, M., and Gidel, G · 2023
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Langevin diffusion variational inference
Geffner, T. and Domke, J · 2023
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On sampling with approximate transport maps
Grenioux, L., Durmus, A., Moulines, É., and Gabrié, M · 2023
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Rigid body flows for sampling molecular crystal structures
Köhler, J., Invernizzi, M., De Haan, P., and Noé, F · 2023
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A theory of continuous generative flow networks
Lahlou, S., Deleu, T., Lemos, P., Zhang, D., Volokhova, A., Hernández-García, A., Ezzine, L. N., Bengio, Y., and Malkin, N · 2023
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Improving gradient-guided nested sampling for posterior inference
Lemos, P., Malkin, N., Handley, W., Bengio, Y., Hezaveh, Y., and Perreault-Levasseur, L · 2023
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Flow matching for generative modeling
Lipman, Y., Chen, R. T. Q., Ben-Hamu, H., Nickel, M., and Le, M · 2023
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GFlowNets and variational inference
Malkin, N., Lahlou, S., Deleu, T., Ji, X., Hu, E., Everett, K., Zhang, D., and Bengio, Y · 2023
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Loss-guided diffusion models for plug-and-play controllable generation
Song, J., Zhang, Q., Yin, H., Mardani, M., Liu, M.-Y., Kautz, J., Chen, Y., and Vahdat, A · 2023
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Improving and generalizing flow-based generative models with minibatch optimal transport
Tong, A., Malkin, N., Huguet, G., Zhang, Y., Rector-Brooks, J., Fatras, K., Wolf, G., and Bengio, Y · 2023
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Denoising diffusion samplers
Vargas, F., Grathwohl, W., and Doucet, A · 2023
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Unifying generative models with GFlowNets and beyond
Zhang, D., Chen, R. T. Q., Malkin, N., and Bengio, Y · 2023
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SE(3)-stochastic flow matching for protein backbone generation
Bose, A. J., Akhound-Sadegh, T., Fatras, K., Huguet, G., Rector-Brooks, J., Liu, C.-H., Nica, A. C., Korablyov, M., Bronstein, M., and Tong, A · 2024
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Reverse diffusion Monte Carlo
Huang, X., Dong, H., Hao, Y., Ma, Y.-A., and Zhang, T · 2024
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Improved sampling via learned diffusions
Richter, L., Berner, J., and Liu, G.-H · 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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