[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
Neural network-based processing and reconstruction of compromised biophotonic image data
In recent years, the integration of deep learning techniques with biophotonic setups has
opened new horizons in bioimaging. A compelling trend in this field involves deliberately …
opened new horizons in bioimaging. A compelling trend in this field involves deliberately …
Bayesian imaging using plug & play priors: when langevin meets tweedie
Since the seminal work of Venkatakrishnan, Bouman, and Wohlberg [Proceedings of the
Global Conference on Signal and Information Processing, IEEE, 2013, pp. 945--948] in …
Global Conference on Signal and Information Processing, IEEE, 2013, pp. 945--948] in …
Deep bayesian inversion
Characterizing statistical properties of solutions of inverse problems is essential for decision
making. Bayesian inversion offers a tractable framework for this purpose, but current …
making. Bayesian inversion offers a tractable framework for this purpose, but current …
Deep probabilistic imaging: Uncertainty quantification and multi-modal solution characterization for computational imaging
Computational image reconstruction algorithms generally produce a single image without
any measure of uncertainty or confidence. Regularized Maximum Likelihood (RML) and …
any measure of uncertainty or confidence. Regularized Maximum Likelihood (RML) and …
Fixed point strategies in data science
The goal of this article is to promote the use of fixed point strategies in data science by
showing that they provide a simplifying and unifying framework to model, analyze, and solve …
showing that they provide a simplifying and unifying framework to model, analyze, and solve …
Image reconstruction algorithms in radio interferometry: From handcrafted to learned regularization denoisers
We introduce a new class of iterative image reconstruction algorithms for radio
interferometry, at the interface of convex optimization and deep learning, inspired by plug …
interferometry, at the interface of convex optimization and deep learning, inspired by plug …
Uncertainty calibrations of deep-learning schemes for full-wave inverse scattering problems
S He, G Zhang, Z Wei - IEEE Transactions on Geoscience and …, 2023 - ieeexplore.ieee.org
Recently, deep learning methods have attracted intensive attentions on solving inverse
scattering problems (ISPs). However, different with traditional physical-model-based …
scattering problems (ISPs). However, different with traditional physical-model-based …
Regularization, Bayesian inference, and machine learning methods for inverse problems
A Mohammad-Djafari - Entropy, 2021 - mdpi.com
Classical methods for inverse problems are mainly based on regularization theory, in
particular those, that are based on optimization of a criterion with two parts: a data-model …
particular those, that are based on optimization of a criterion with two parts: a data-model …
Comparison of classical and Bayesian imaging in radio interferometry-Cygnus A with CLEAN and resolve
CLEAN, the commonly employed imaging algorithm in radio interferometry, suffers from a
number of shortcomings: In its basic version, it does not have the concept of diffuse flux, and …
number of shortcomings: In its basic version, it does not have the concept of diffuse flux, and …