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Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty
  Quantification

Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty Quantification

30 May 2023
Yifei Liu
Rex Shen
Xiaotong Shen
    DiffM
ArXivPDFHTML

Papers citing "Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty Quantification"

4 / 4 papers shown
Title
Diffusion Models are Minimax Optimal Distribution Estimators
Diffusion Models are Minimax Optimal Distribution Estimators
Kazusato Oko
Shunta Akiyama
Taiji Suzuki
DiffM
61
84
0
03 Mar 2023
RePaint: Inpainting using Denoising Diffusion Probabilistic Models
RePaint: Inpainting using Denoising Diffusion Probabilistic Models
Andreas Lugmayr
Martin Danelljan
Andrés Romero
F. I. F. Richard Yu
Radu Timofte
Luc Van Gool
DiffM
200
1,330
0
24 Jan 2022
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
268
5,635
0
05 Dec 2016
Efficient Estimation of Word Representations in Vector Space
Efficient Estimation of Word Representations in Vector Space
Tomáš Mikolov
Kai Chen
G. Corrado
J. Dean
3DV
228
29,632
0
16 Jan 2013
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