Fetching the paper…
Reading the bibliography…
In this work, a method for obtaining pixel-wise error bounds in Bayesian regularization of inverse imaging problems is introduced.
“Note on a Method for Calculating Corrected Sums of Squares and Products”
B.. Welford · 1962
Earlier work this paper cites.
“Reverend Bayes on inference engines: A distributed hierarchical approach”
J. Pearl · 1982
Earlier work this paper cites.
“Nonlinear total variation based noise removal algorithms”
Leonid Rudin, Stanley Osher and Emad Fatemi · 1992
Earlier work this paper cites.
“Graphical Models”
Steffen. Lauritzen · 1996
Earlier work this paper cites.
“Exponential convergence of Langevin distributions and their discrete approximations”
Gareth Roberts and Richard Tweedie · 1996
Earlier work this paper cites.
“Exponential convergence of Langevin distributions and their discrete approximations”
Gareth. Roberts and Richard. Tweedie · 1996
Earlier work this paper cites.
“Image recovery via total variation minimization and related problems”
Antonin Chambolle and Pierre-Louis Lions · 1997
Earlier work this paper cites.
“Paul Langevin’s 1908 paper “On the Theory of Brownian Motion” [“Sur la théorie du mouvement brownien,” C. R. Acad. Sci. (Paris) 146, 530–533 (1908)]”
Don. Lemons and Anthony Gythiel · 1997
Earlier work this paper cites.
“Monte Carlo statistical methods”
Christian Robert, George Casella and George Casella · 1999
Earlier work this paper cites.
“Robust wavelet denoising”
Sylvain Sardy, Paul Tseng and Andrew Bruce · 2001
Earlier work this paper cites.
“Recursive computation of the invariant distribution of a diffusion”
Damien Lamberton and Gilles Pages · 2002
Earlier work this paper cites.
“Recursive computation of the invariant distribution of a diffusion: the case of a weakly mean reverting drift”
Damien Lamberton and Gilles Pages · 2003
Earlier work this paper cites.
“Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters”
Marshall Tappen and Freeman T · 2003
Earlier work this paper cites.
“An iterative thresholding algorithm for linear inverse problems with a sparsity constraint”
Ingrid Daubechies, Michel Defrise and Christine De · 2004
Earlier work this paper cites.
“Estimating mutual information”
Alexander Kraskov, Harald Stögbauer and Peter Grassberger · 2004
Earlier work this paper cites.
“Algorithmic learning in a random world”
Vladimir Vovk, Alexander Gammerman and Glenn Shafer · 2005
Earlier work this paper cites.
“Natural image denoising with convolutional networks”
Viren Jain and Sebastian Seung · 2008
Earlier work this paper cites.
“A Tutorial on Conformal Prediction”
Glenn Shafer and Vladimir Vovk · 2008
Earlier work this paper cites.
“Imagenet: A large-scale hierarchical image database”
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei · 2009
Earlier work this paper cites.
“Fields of experts”
Stefan Roth and Michael Black · 2009
Earlier work this paper cites.
“A first-order primal-dual algorithm for convex problems with applications to imaging”
Antonin Chambolle and Thomas Pock · 2011
Cited alongside, same era.
“Numerical Solution of Stochastic Differential Equations”, Stochastic Modelling and Applied Probability
P.E. Kloeden and E. Platen · 2013
Cited alongside, same era.
“Distribution-free prediction sets”
Jing Lei, James Robins and Larry Wasserman · 2013
Cited alongside, same era.
“Insights into analysis operator learning: From patch-based sparse models to higher order MRFs”
Yunjin Chen, Rene Ranftl and Thomas Pock · 2014
Cited alongside, same era.
“Weight uncertainty in neural network”
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu and Daan Wierstra · 2015
Cited alongside, same era.
“A conformal prediction approach to explore functional data”
Jing Lei, Alessandro Rinaldo and Larry Wasserman · 2015
“Conformalized quantile regression”
Yaniv Romano, Evan Patterson and Emmanuel Candes · 2019
Later among the works it cites.
“Least ambiguous set-valued classifiers with bounded error levels”
Mauricio Sadinle, Jing Lei and Larry Wasserman · 2019
Later among the works it cites.
“Generative modeling by estimating gradients of the data distribution”
Yang Song and Stefano Ermon · 2019
Later among the works it cites.
“Conformal prediction under covariate shift”
Ryan Tibshirani, Rina Foygel, Emmanuel Candes and Aaditya Ramdas · 2019
Later among the works it cites.
“Higher-order total variation approaches and generalisations”
Kristian Bredies and Martin Holler · 2020
Later among the works it cites.
“fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning”
Florian Knoll, Jure Zbontar, Anuroop Sriram, Matthew. Muckley, Mary Bruno, Aaron Defazio, Marc Parente, Krzysztof. Geras, Joe Katsnelson, Hersh Chandarana, Zizhao Zhang, Michal Drozdzalv, Adriana Romero, Michael Rabbat, Pascal Vincent, James Pinkerton, Duo Wang, Nafissa Yakubova, Erich Owens, C.Lawrence Zitnick, Michael. Recht, Daniel. Sodickson and Yvonne. Lui · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
“An introduction to continuous optimization for imaging”
Antonin Chambolle and Thomas Pock · 2016
Cited alongside, same era.
“Dropout as a bayesian approximation: Representing model uncertainty in deep learning”
Yarin Gal and Zoubin Ghahramani · 2016
Cited alongside, same era.
“Proximal markov chain monte carlo algorithms”
Marcelo Pereyra · 2016
Cited alongside, same era.
“Compressed sensing using generative models”
Ashish Bora, Ajil Jalal, Eric Price and Alexandros Dimakis · 2017
Cited alongside, same era.
“What uncertainties do we need in bayesian deep learning for computer vision?”
Alex Kendall and Yarin Gal · 2017
Cited alongside, same era.
“Photo-realistic single image super-resolution using a generative adversarial network”
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz and Zehan Wang · 2017
Cited alongside, same era.
Later among the works it cites.
“Denoising score-matching for uncertainty quantification in inverse problems”
Zaccharie Ramzi, Benjamin Rémy, Francois Lanusse, Jean-Luc Starck and Philippe Ciuciu · 2020
Later among the works it cites.
“A review of uncertainty quantification in deep learning: Techniques, applications and challenges”
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi and U Acharya · 2021
Later among the works it cites.
“Learn then test: Calibrating predictive algorithms to achieve risk control”
Anastasios Angelopoulos, Stephen Bates, Emmanuel Candès, Michael Jordan and Lihua Lei · 2021
Later among the works it cites.
“Distribution-free, risk-controlling prediction sets”
Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik and Michael Jordan · 2021
Later among the works it cites.
“Score-based generative modeling with critically-damped langevin diffusion”
Tim Dockhorn, Arash Vahdat and Karsten Kreis · 2021
Later among the works it cites.
“Total deep variation: A stable regularization method for inverse problems”
Erich Kobler, Alexander Effland, Karl Kunisch and Thomas Pock · 2021
Later among the works it cites.
“Distribution-free conditional median inference”
Dhruv Medarametla and Emmanuel Candès · 2021
Later among the works it cites.
“Bayesian uncertainty estimation of learned variational MRI reconstruction”
Dominik Narnhofer, Alexander Effland, Erich Kobler, Kerstin Hammernik, Florian Knoll and Thomas Pock · 2021
Later among the works it cites.
Anastasios Angelopoulos, Stephen Bates, Adam Fisch, Lihua Lei and Tal Schuster · 2022
Closest in time.
“Image-to-image regression with distribution-free uncertainty quantification and applications in imaging”
Anastasios Angelopoulos, Amit Kohli, Stephen Bates, Michael Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula and Yaniv Romano · 2022
Closest in time.
“A generative variational model for inverse problems in imaging”
Andreas Habring and Martin Holler · 2022
Closest in time.
“Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie”
Rémi Laumont, Valentin Bortoli, Andrés Almansa, Julie Delon, Alain Durmus and Marcelo Pereyra · 2022
Closest in time.
“MRI Reconstruction via Data Driven Markov Chain with Joint Uncertainty Estimation”
Guanxiong Luo, Martin Heide and Martin Uecker · 2022
Closest in time.
“Computed Tomography Reconstruction using Generative Energy-Based Priors”
Martin Zach, Erich Kobler and Thomas Pock · 2022
Closest in time.