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2311.09639
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On the Quantification of Image Reconstruction Uncertainty without Training Data
16 November 2023
Sirui Bi
Victor Fung
Jiaxin Zhang
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Papers citing
"On the Quantification of Image Reconstruction Uncertainty without Training Data"
6 / 6 papers shown
Title
Simulation Intelligence: Towards a New Generation of Scientific Methods
Alexander Lavin
D. Krakauer
Hector Zenil
Justin Emile Gottschlich
Tim Mattson
...
A. Hanuka
Manuela Veloso
Samuel A. Assefa
Stephan Zheng
Avi Pfeffer
40
104
0
06 Dec 2021
Intermediate Layer Optimization for Inverse Problems using Deep Generative Models
Giannis Daras
Joseph Dean
A. Jalal
A. Dimakis
DRL
183
83
0
15 Feb 2021
Solving Inverse Problems with a Flow-based Noise Model
Jay Whang
Qi Lei
A. Dimakis
56
36
0
18 Mar 2020
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Mohammad Emtiyaz Khan
Didrik Nielsen
Voot Tangkaratt
Wu Lin
Y. Gal
Akash Srivastava
ODL
74
266
0
13 Jun 2018
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
268
5,660
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
247
9,134
0
06 Jun 2015
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