Uncertainty Quantification via Neural Posterior Principal Components

27 September 2023

Papers citing "Uncertainty Quantification via Neural Posterior Principal Components"

22 / 22 papers shown

Title
Uncertainty Quantification for Machine Learning in Healthcare: A Survey L. J. L. Lopez Shaza Elsharief Dhiyaa Al Jorf Firas Darwish Congbo Ma Farah E. Shamout 110 0 0 04 May 2025
InvFussion: Bridging Supervised and Zero-shot Diffusion for Inverse Problems Noam Elata Hyungjin Chung Jong Chul Ye T. Michaeli Michael Elad DiffM 40 0 0 02 Apr 2025
Conformal Risk Control for Semantic Uncertainty Quantification in Computed Tomography Jacopo Teneggi J. W. Stayman Jeremias Sulam 64 1 0 28 Feb 2025
Conditional Distribution Quantization in Machine Learning Blaise Delattre Sylvain Delattre Alexandre Verine Alexandre Allauzen 50 0 0 11 Feb 2025
pcaGAN: Improving Posterior-Sampling cGANs via Principal Component Regularization Matthew Bendel Rizwan Ahmad P. Schniter MedIm DiffM 36 1 0 01 Nov 2024
Hierarchical Uncertainty Exploration via Feedforward Posterior Trees E. Nehme Rotem Mulayoff T. Michaeli UQCV 48 2 0 24 May 2024
Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion Hila Manor T. Michaeli DiffM 26 25 0 15 Feb 2024
Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance Xinyu Peng Ziyang Zheng Wenrui Dai Nuoqian Xiao Chenglin Li Junni Zou Hongkai Xiong DiffM 39 21 0 03 Feb 2024
Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction Ziqi Ma Kamyar Azizzadenesheli A. Anandkumar 21 6 0 02 Feb 2024
Uncertainty Visualization via Low-Dimensional Posterior Projections Omer Yair E. Nehme T. Michaeli UQCV 38 2 0 12 Dec 2023
On the Posterior Distribution in Denoising: Application to Uncertainty Quantification Hila Manor T. Michaeli UQCV 23 17 0 24 Sep 2023
Score-Based Diffusion Models as Principled Priors for Inverse Imaging Berthy T. Feng Jamie Smith Michael Rubinstein Huiwen Chang Katherine L. Bouman William T. Freeman DiffM 87 87 0 23 Apr 2023
GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration Naoki Murata Koichi Saito Chieh-Hsin Lai Yuhta Takida Toshimitsu Uesaka Yuki Mitsufuji Stefano Ermon DiffM 56 49 0 30 Jan 2023
Denoising Diffusion Restoration Models Bahjat Kawar Michael Elad Stefano Ermon Jiaming Song DiffM 215 783 0 27 Jan 2022
RePaint: Inpainting using Denoising Diffusion Probabilistic Models Andreas Lugmayr Martin Danelljan Andrés Romero F. I. F. Richard Yu Radu Timofte Luc Van Gool DiffM 233 1,355 0 24 Jan 2022
Palette: Image-to-Image Diffusion Models Chitwan Saharia William Chan Huiwen Chang Chris A. Lee Jonathan Ho Tim Salimans David J. Fleet Mohammad Norouzi DiffM VLM 342 1,591 0 10 Nov 2021
Distribution-Free, Risk-Controlling Prediction Sets Stephen Bates Anastasios Nikolas Angelopoulos Lihua Lei Jitendra Malik Michael I. Jordan OOD 187 186 0 07 Jan 2021
Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed Eric Luhman Troy Luhman DiffM 195 258 0 07 Jan 2021
A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras S. Laine Timo Aila 285 10,354 0 12 Dec 2018
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,661 0 05 Dec 2016
Image-to-Image Translation with Conditional Adversarial Networks Phillip Isola Jun-Yan Zhu Tinghui Zhou Alexei A. Efros SSeg 212 19,450 0 21 Nov 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015