Continual repeated annealed flow transport Monte Carlo

Autor: Matthews, AGDG, Arbel, M, Rezende, DJ, Doucet, A

Publikováno v: International Conference on Machine Learning 2022
International Conference on Machine Learning 2022, 2022, Baltimore, United States

We propose Continual Repeated Annealed Flow Transport Monte Carlo (CRAFT), a method that combines a sequential Monte Carlo (SMC) sampler (itself a generalization of Annealed Importance Sampling) with variational inference using normalizing flows. The

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ac85c7d24fc136babacfb00ce22a316f
https://ora.ox.ac.uk/objects/uuid:285d6cb2-7c2c-4236-bd5d-311cade28945

Annealed Flow Transport Monte Carlo

Autor: Arbel, M, Matthews, AGDG, Doucet, A

Publikováno v: ICML 2021-38th International Conference on Machine Learning
ICML 2021-38th International Conference on Machine Learning, Jul 2021, Online, France. pp.1-70

International audience; Annealed Importance Sampling (AIS) and its Sequential Monte Carlo (SMC) extensions are state-of-the-art methods for estimating normalizing constants of probability distributions. We propose here a novel Monte Carlo algorithm,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e83171dac2d8aed7f7007d8da22c8e72
http://arxiv.org/abs/2102.07501

Enhanced super-radiant emission of FEM near waveguide-cutoff and near zero-slippage conditions

Autor: Arbel, M. ^*, Eichenbaum, A.L., Pinhasi, Y., Lurie, Y., Abramovich, A., Kleinman, H., Yakover, I.M., Gover, A.

Publikováno v: In Nuclear Inst. and Methods in Physics Research, A 2002 483(1):220-224

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