UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography

22 February 2022

Papers citing "UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography"

9 / 9 papers shown

Title
Regularized Multi-Decoder Ensemble for an Error-Aware Scene Representation Network Tianyu Xiong Skylar W. Wurster Hanqi Guo Tom Peterka Han-Wei Shen UQCV 35 1 0 26 Jul 2024
Conditional Injective Flows for Bayesian Imaging AmirEhsan Khorashadizadeh K. Kothari Leonardo Salsi Ali Aghababaei Harandi Maarten V. de Hoop Ivan Dokmanić MedIm 16 16 0 15 Apr 2022
Wide Mean-Field Bayesian Neural Networks Ignore the Data Beau Coker W. Bruinsma David R. Burt Weiwei Pan Finale Doshi-Velez UQCV BDL 29 21 0 23 Feb 2022
Posterior temperature optimized Bayesian models for inverse problems in medical imaging M. Laves Malte Tolle Alexander Schlaefer Sandy Engelhardt 23 10 0 02 Feb 2022
From data to functa: Your data point is a function and you can treat it like one Emilien Dupont Hyunjik Kim S. M. Ali Eslami Danilo Jimenez Rezende Dan Rosenbaum TDI 3DPC 160 136 0 28 Jan 2022
Local Implicit Grid Representations for 3D Scenes C. Jiang Avneesh Sud A. Makadia Jingwei Huang Matthias Nießner Thomas Funkhouser 3DPC 214 556 0 19 Mar 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,635 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,042 0 06 Jun 2015
MCMC using Hamiltonian dynamics Radford M. Neal 130 3,260 0 09 Jun 2012