Span of regularization for solution of inverse problems with application to magnetic resonance relaxometry of the brain. [PDF]
Sci Rep, 2022 Bi C, Ou MY, Bouhrara M, Spencer RG.
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Generative modelling meets Bayesian inference: a new paradigm for inverse problems. [PDF]
Philos Trans A Math Phys Eng SciOliviero-Durmus A +4 more
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A Graph-Space Optimal Transport Approach Based on Kaniadakis κ-Gaussian Distribution for Inverse Problems Related to Wave Propagation. [PDF]
Entropy (Basel), 2023 da Silva SLEF +3 more
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Denoising: a powerful building block for imaging, inverse problems and machine learning. [PDF]
Philos Trans A Math Phys Eng SciMilanfar P, Delbracio M.
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Regularization, Bayesian Inference, and Machine Learning Methods for Inverse Problems. [PDF]
Entropy (Basel), 2021 Mohammad-Djafari A.
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Information-distilled physics informed deep learning for high order differential inverse problems with extreme discontinuities. [PDF]
Commun EngPeng M, Tang H.
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On Ambiguity in Linear Inverse Problems: Entrywise Bounds on Nearly Data-Consistent Solutions and Entrywise Condition Numbers. [PDF]
IEEE Trans Signal Process, 2023 Haldar JP.
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Spot the bot: the inverse problems of NLP. [PDF]
PeerJ Comput SciGromov VA +3 more
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Equivariant neural networks for inverse problems. [PDF]
Inverse Probl, 2021 Celledoni E +5 more
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Inverse Fluid Convection Problems in Enclosures
Journal of Applied Mathematics, 2012 Fu-Yun Zhao, Di Liu, Steve H. L. Yim
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