Posterior-Variance-Based Error Quantification for Inverse Problems in Imaging

23 December 2022

Papers citing "Posterior-Variance-Based Error Quantification for Inverse Problems in Imaging"

29 / 29 papers shown

Title
Diffusion at Absolute Zero: Langevin Sampling Using Successive Moreau Envelopes [conference paper] Andreas Habring Alexander Falk Thomas Pock 82 0 0 03 Feb 2025
FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transforms L. Bogensperger Dominik Narnhofer Alexander Falk Konrad Schindler Thomas Pock MedIm 142 3 0 28 May 2024
Conformal Risk Control Anastasios Nikolas Angelopoulos Stephen Bates Adam Fisch Lihua Lei Tal Schuster 122 132 0 04 Aug 2022
Computed Tomography Reconstruction using Generative Energy-Based Priors Martin Zach Erich Kobler Thomas Pock DiffM MedIm 27 12 0 23 Mar 2022
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging Anastasios Nikolas Angelopoulos Amit Kohli Stephen Bates Michael I. Jordan Jitendra Malik Thayer Alshaabi Srigokul Upadhyayula Yaniv Romano UQCV OOD 66 94 0 10 Feb 2022
Score-Based Generative Modeling with Critically-Damped Langevin Diffusion Tim Dockhorn Arash Vahdat Karsten Kreis DiffM 71 231 0 14 Dec 2021
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control Anastasios Nikolas Angelopoulos Stephen Bates Emmanuel J. Candès Michael I. Jordan Lihua Lei 184 129 0 03 Oct 2021
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie R. Laumont Valentin De Bortoli Andrés Almansa J. Delon Alain Durmus Marcelo Pereyra 41 111 0 08 Mar 2021
Distribution-Free Conditional Median Inference Dhruv Medarametla Emmanuel J. Candès 47 15 0 16 Feb 2021
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction Dominik Narnhofer Alexander Effland Erich Kobler Kerstin Hammernik Florian Knoll Thomas Pock UQCV BDL 40 50 0 12 Feb 2021
Distribution-Free, Risk-Controlling Prediction Sets Stephen Bates Anastasios Nikolas Angelopoulos Lihua Lei Jitendra Malik Michael I. Jordan OOD 232 193 0 07 Jan 2021
Denoising Score-Matching for Uncertainty Quantification in Inverse Problems Zaccharie Ramzi B. Remy F. Lanusse Jean-Luc Starck P. Ciuciu UQCV MedIm 58 14 0 16 Nov 2020
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges Moloud Abdar Farhad Pourpanah Sadiq Hussain Dana Rezazadegan Li Liu ... Xiaochun Cao Abbas Khosravi U. Acharya V. Makarenkov S. Nahavandi BDL UQCV 190 1,893 0 12 Nov 2020
Generative Modeling by Estimating Gradients of the Data Distribution Yang Song Stefano Ermon SyDa DiffM 163 3,803 0 12 Jul 2019
Conformalized Quantile Regression Yaniv Romano Evan Patterson Emmanuel J. Candès 229 603 0 08 May 2019
MoDL: Model Based Deep Learning Architecture for Inverse Problems H. Aggarwal M. Mani M. Jacob 108 1,013 0 07 Dec 2017
Learning a Variational Network for Reconstruction of Accelerated MRI Data Kerstin Hammernik Teresa Klatzer Erich Kobler M. Recht D. Sodickson Thomas Pock Florian Knoll 43 1,535 0 03 Apr 2017
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? Alex Kendall Y. Gal BDL OOD UD UQCV PER 269 4,667 0 15 Mar 2017
Compressed Sensing using Generative Models Ashish Bora A. Jalal Eric Price A. Dimakis 92 804 0 09 Mar 2017
Efficient Bayesian computation by proximal Markov chain Monte Carlo: when Langevin meets Moreau Alain Durmus Eric Moulines Marcelo Pereyra 51 176 0 22 Dec 2016
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network C. Ledig Lucas Theis Ferenc Huszár Jose Caballero Andrew Cunningham ... Andrew P. Aitken Alykhan Tejani J. Totz Zehan Wang Wenzhe Shi GAN 229 10,646 0 15 Sep 2016
Least Ambiguous Set-Valued Classifiers with Bounded Error Levels Mauricio Sadinle Jing Lei Larry A. Wasserman 110 239 0 02 Sep 2016
High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm Alain Durmus Eric Moulines 71 352 0 05 May 2016
Distribution-Free Predictive Inference For Regression Jing Lei M. G'Sell Alessandro Rinaldo Robert Tibshirani Larry A. Wasserman 198 832 0 14 Apr 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 470 9,233 0 06 Jun 2015
Insights into analysis operator learning: From patch-based sparse models to higher-order MRFs Yunjin Chen René Ranftl Thomas Pock 46 114 0 13 Jan 2014
Proximal Markov chain Monte Carlo algorithms Marcelo Pereyra 56 178 0 02 Jun 2013
A Conformal Prediction Approach to Explore Functional Data Jing Lei Alessandro Rinaldo Larry A. Wasserman 115 128 0 26 Feb 2013
A tutorial on conformal prediction Glenn Shafer V. Vovk 257 1,122 0 21 Jun 2007