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Batch-Less Stochastic Gradient Descent for Compressive Learning of Deep Regularization for Image Denoising

Overview of attention for article published in Journal of Mathematical Imaging and Vision, March 2024
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Title
Batch-Less Stochastic Gradient Descent for Compressive Learning of Deep Regularization for Image Denoising
Published in
Journal of Mathematical Imaging and Vision, March 2024
DOI 10.1007/s10851-024-01178-x
Authors

Hui Shi, Yann Traonmilin, Jean-François Aujol

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Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 March 2024.
All research outputs
#16,840,561
of 25,543,275 outputs
Outputs from Journal of Mathematical Imaging and Vision
#170
of 341 outputs
Outputs of similar age
#91,480
of 192,565 outputs
Outputs of similar age from Journal of Mathematical Imaging and Vision
#1
of 1 outputs
Altmetric has tracked 25,543,275 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 341 research outputs from this source. They receive a mean Attention Score of 2.7. This one is in the 47th percentile – i.e., 47% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 192,565 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them