ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1405.1164
  4. Cited By
Stein Unbiased GrAdient estimator of the Risk (SUGAR) for multiple
  parameter selection

Stein Unbiased GrAdient estimator of the Risk (SUGAR) for multiple parameter selection

6 May 2014
Charles-Alban Deledalle
Samuel Vaiter
M. Fadili
Gabriel Peyré
ArXivPDFHTML

Papers citing "Stein Unbiased GrAdient estimator of the Risk (SUGAR) for multiple parameter selection"

17 / 17 papers shown
Title
MARS: Meta-Learning as Score Matching in the Function Space
MARS: Meta-Learning as Score Matching in the Function Space
Krunoslav Lehman Pavasovic
Jonas Rothfuss
Andreas Krause
BDL
23
4
0
24 Oct 2022
Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning
Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning
Christian Raymond
Qi Chen
Bing Xue
Mengjie Zhang
FedML
27
11
0
19 Sep 2022
Fixed-Point Automatic Differentiation of Forward--Backward Splitting
  Algorithms for Partly Smooth Functions
Fixed-Point Automatic Differentiation of Forward--Backward Splitting Algorithms for Partly Smooth Functions
Sheheryar Mehmood
Peter Ochs
33
3
0
05 Aug 2022
Automatic differentiation of nonsmooth iterative algorithms
Automatic differentiation of nonsmooth iterative algorithms
Jérôme Bolte
Edouard Pauwels
Samuel Vaiter
15
21
0
31 May 2022
Alternative design of DeepPDNet in the context of image restoration
Alternative design of DeepPDNet in the context of image restoration
Mingyuan Jiu
N. Pustelnik
25
2
0
20 Feb 2022
Fast Scalable Image Restoration using Total Variation Priors and
  Expectation Propagation
Fast Scalable Image Restoration using Total Variation Priors and Expectation Propagation
D. Yao
S. Mclaughlin
Y. Altmann
11
6
0
04 Oct 2021
Investigating Bi-Level Optimization for Learning and Vision from a
  Unified Perspective: A Survey and Beyond
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond
Risheng Liu
Jiaxin Gao
Jin Zhang
Deyu Meng
Zhouchen Lin
AI4CE
43
221
0
27 Jan 2021
An off-the-grid approach to multi-compartment magnetic resonance
  fingerprinting
An off-the-grid approach to multi-compartment magnetic resonance fingerprinting
Mohammad Golbabaee
C. Poon
6
4
0
23 Nov 2020
Automated data-driven selection of the hyperparameters for
  Total-Variation based texture segmentation
Automated data-driven selection of the hyperparameters for Total-Variation based texture segmentation
Barbara Pascal
Samuel Vaiter
N. Pustelnik
P. Abry
24
18
0
20 Apr 2020
Deep Learning for space-variant deconvolution in galaxy surveys
Deep Learning for space-variant deconvolution in galaxy surveys
F. Sureau
Alexis Lechat
Jean-Luc Starck
3DPC
17
21
0
01 Nov 2019
Extending Stein's unbiased risk estimator to train deep denoisers with
  correlated pairs of noisy images
Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy images
Magauiya Zhussip
Shakarim Soltanayev
S. Chun
OOD
10
3
0
07 Feb 2019
Deep Unfolding of a Proximal Interior Point Method for Image Restoration
Deep Unfolding of a Proximal Interior Point Method for Image Restoration
Carla Bertocchi
Émilie Chouzenoux
M. Corbineau
J. Pesquet
M. Prato
16
107
0
11 Dec 2018
Hyperparameter optimization with approximate gradient
Hyperparameter optimization with approximate gradient
Fabian Pedregosa
17
441
0
07 Feb 2016
On debiasing restoration algorithms: applications to total-variation and
  nonlocal-means
On debiasing restoration algorithms: applications to total-variation and nonlocal-means
Charles-Alban Deledalle
Nicolas Papadakis
Joseph Salmon
35
22
0
05 Mar 2015
The Degrees of Freedom of Partly Smooth Regularizers
The Degrees of Freedom of Partly Smooth Regularizers
Samuel Vaiter
Charles-Alban Deledalle
M. Fadili
Gabriel Peyré
C. Dossal
89
49
0
22 Apr 2014
The Projected GSURE for Automatic Parameter Tuning in Iterative
  Shrinkage Methods
The Projected GSURE for Automatic Parameter Tuning in Iterative Shrinkage Methods
Raja Giryes
Michael Elad
Yonina C. Eldar
87
129
0
21 Mar 2010
A SURE Approach for Digital Signal/Image Deconvolution Problems
A SURE Approach for Digital Signal/Image Deconvolution Problems
J. Pesquet
A. Benazza-Benyahia
C. Chaux
81
91
0
27 Oct 2008
1