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Proximal operator
In mathematical optimization, the proximal operator is an operator associated with a convex function defined by: It is frequently used in…
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Related topics
Related topics
4 relations
Convex function
Mathematical optimization
Proximal gradient method
Total variation denoising
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
2019
2019
Learning to Solve Linear Inverse Problems in Imaging with Neumann Networks
Greg Ongie
,
Davis Gilton
,
R. Willett
2019
Corpus ID: 208633261
Recent advances have illustrated that it is often possible to learn to solve linear inverse problems in imaging using training…
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2017
2017
Beamforming-deconvolution: A novel concept of deconvolution for ultrasound imaging
Zhouye Chen
,
Adrien Besson
,
J. Thiran
,
Y. Wiaux
2017
Corpus ID: 41259742
In ultrasound (US) imaging, beamforming is usually separated from the deconvolution or some other post-processing techniques. The…
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2017
2017
Learning Convolutional Proximal Filters
U. Kamilov
,
H. Mansour
,
Dehong Liu
2017
Corpus ID: 31802539
In the past decade, sparsity-driven methods have led to substantial improvements in the capabilities of numerous imaging systems…
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2016
2016
Training Ternary Neural Networks with Exact Proximal Operator
Penghang Yin
,
Shuai Zhang
,
J. Xin
,
Y. Qi
arXiv.org
2016
Corpus ID: 14944444
In this paper, we propose a stochastic proximal gradient method to train ternary weight neural networks (TNN). The proposed…
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2016
2016
Knowledge-aided object-oriented three-dimensional microwave imaging
Longgang Wang
,
Lianlin Li
,
Xiaoyang Zhou
,
T. Cui
,
A. Nehorai
URSI Asia-Pacific Radio Science Conference
2016
Corpus ID: 25964532
For the past few years, researchers hold a strong interests on knowledge-aided object-oriented high-resolution microwave imaging…
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2016
2016
Reweighted stochastic learning
V. Jumutc
,
J. Suykens
Neurocomputing
2016
Corpus ID: 10327418
2016
2016
Minimizing the Maximal Rank
E. Bylow
,
Carl Olsson
,
Fredrik Kahl
,
M. Nilsson
Computer Vision and Pattern Recognition
2016
Corpus ID: 17569199
In computer vision, many problems can be formulated as finding a low rank approximation of a given matrix. Ideally, if all…
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2015
2015
Un-regularizing: approximate proximal point algorithms for empirical risk minimization
2015
Corpus ID: 16441296
For completeness, in this section we derive the dual (5) to the problem of computing proximal operator for the ERM objective (3).
2014
2014
A convergence proof of the split Bregman method for regularized least-squares problems
Hung Nien
,
J. Fessler
arXiv.org
2014
Corpus ID: 17870210
The split Bregman (SB) method [T. Goldstein and S. Osher, SIAM J. Imaging Sci., 2 (2009), pp. 323-43] is a fast splitting-based…
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2014
2014
A convex formulation for informed source separation in the single channel setting
A. Lefèvre
,
François Glineur
,
P. Absil
Neurocomputing
2014
Corpus ID: 15430732
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