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In recent years, Bayesian inference in large-scale inverse problems found in science, engineering and machine learning has gained significant attention.
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H. Owhadi, C. Scovel, and T. Sullivan · 2015
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The Bayesian approach to inverse problems
M. Dashti and A. M. Stuart · 2016
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Generative adversarial networks
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2020
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f f -GAN: Training generative neural samplers using variational divergence minimization
S. Nowozin, B. Cseke, and R. Tomioka · 2016
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Brittleness of Bayesian inference and new Selberg formulas
H. Owhadi and C. Scovel · 2016
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Well-posed Bayesian inverse problems with infinitely divisible and heavy-tailed prior measures
B. Hosseini · 2017
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Well-posed Bayesian inverse problems: priors with exponential tails
B. Hosseini and N. Nigam · 2017
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Error bounds for approximations of Markov chains used in Bayesian sampling
J. E. Johndrow and J. C. Mattingly · 2017
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Kernel mean embedding of distributions: A review and beyond
K. Muandet, K. Fukumizu, B. Sriperumbudur, B. Schölkopf, et al · 2017
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Deep learning techniques for inverse problems in imaging
G. Ongie, A. Jalal, C. A. Metzler, R. G. Baraniuk, A. G. Dimakis, and R. Willett · 2020
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On the local Lipschitz stability of Bayesian inverse problems
B. Sprungk · 2020
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Convergence of Gaussian process regression with estimated hyper-parameters and applications in Bayesian inverse problems
A. L. Teckentrup · 2020
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A review of uncertainty quantification in deep learning: Techniques, applications and challenges
M. Abdar, F. Pourpanah, S. Hussain, D. Rezazadegan, L. Liu, M. Ghavamzadeh, P. Fieguth, X. Cao, A. Khosravi, U. R. Acharya, et al · 2021
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Neural network approximation
R. DeVore, B. Hanin, and G. Petrova · 2021
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( f , Γ ) (f,\Gamma) -divergences: Interpolating between f f -divergences and integral probability metrics
J. Birrell, P. Dupuis, M. A. Katsoulakis, Y. Pantazis, and L. Rey-Bellet · 2022
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Solving inverse problems by joint posterior maximization with autoencoding prior
M. González, A. Almansa, and P. Tan · 2022
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Spectral gaps and error estimates for infinite-dimensional Metropolis-Hastings with non-Gaussian priors
B. Hosseini and J. E. Johndrow · 2022
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Bayesian imaging using plug & play priors: when Langevin meets Tweedie
R. Laumont, V. D. Bortoli, A. Almansa, J. Delon, A. Durmus, and M. Pereyra · 2022
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Exponential relu dnn expression of holomorphic maps in high dimension
J. A. Opschoor, C. Schwab, and J. Zech · 2022
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Solution of physics-based Bayesian inverse problems with deep generative priors
D. V. Patel, D. Ray, and A. A. Oberai · 2022
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