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Uncertainty Quantification with Generative Models

Uncertainty Quantification with Generative Models

22 October 2019
Vanessa Böhm
F. Lanusse
U. Seljak
ArXivPDFHTML

Papers citing "Uncertainty Quantification with Generative Models"

6 / 6 papers shown
Title
Reliable Trajectory Prediction and Uncertainty Quantification with
  Conditioned Diffusion Models
Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models
Marion Neumeier
Sebastian Dorn
M. Botsch
Wolfgang Utschick
DiffM
43
3
0
23 May 2024
Uncertainty Quantification for Deep Unrolling-Based Computational
  Imaging
Uncertainty Quantification for Deep Unrolling-Based Computational Imaging
Canberk Ekmekci
Müjdat Çetin
UQCV
19
11
0
02 Jul 2022
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty
Paz Fink Shustin
Shashanka Ubaru
Vasileios Kalantzis
L. Horesh
H. Avron
21
2
0
10 Feb 2022
Bridging the Gap Between Explainable AI and Uncertainty Quantification
  to Enhance Trustability
Bridging the Gap Between Explainable AI and Uncertainty Quantification to Enhance Trustability
Dominik Seuss
17
15
0
25 May 2021
Denoising Score-Matching for Uncertainty Quantification in Inverse
  Problems
Denoising Score-Matching for Uncertainty Quantification in Inverse Problems
Zaccharie Ramzi
B. Remy
F. Lanusse
Jean-Luc Starck
P. Ciuciu
UQCV
MedIm
30
14
0
16 Nov 2020
Probabilistic Autoencoder
Probabilistic Autoencoder
Vanessa Böhm
U. Seljak
UQCV
BDL
DRL
16
32
0
09 Jun 2020
1