↓ Skip to main content

SALR: Sharpness-Aware Learning Rate Scheduler for Improved Generalization

Overview of attention for article published in IEEE Transactions on Neural Networks and Learning Systems, September 2024
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

twitter
5 X users

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
12 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
SALR: Sharpness-Aware Learning Rate Scheduler for Improved Generalization
Published in
IEEE Transactions on Neural Networks and Learning Systems, September 2024
DOI 10.1109/tnnls.2023.3263393
Pubmed ID
Authors

Xubo Yue, Maher Nouiehed, Raed Al Kontar

Timeline

Login to access the full chart related to this output.

If you don’t have an account, click here to discover Explorer

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 25%
Researcher 2 17%
Student > Ph. D. Student 1 8%
Unknown 6 50%
Readers by discipline Count As %
Computer Science 4 33%
Physics and Astronomy 1 8%
Engineering 1 8%
Unknown 6 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 18 November 2020.
All research outputs
#15,054,752
of 26,588,416 outputs
Outputs from IEEE Transactions on Neural Networks and Learning Systems
#1,360
of 3,454 outputs
Outputs of similar age
#46,914
of 142,542 outputs
Outputs of similar age from IEEE Transactions on Neural Networks and Learning Systems
#3
of 20 outputs
Altmetric has tracked 26,588,416 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,454 research outputs from this source. They receive a mean Attention Score of 2.8. This one has gotten more attention than average, scoring higher than 60% of its peers.
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 142,542 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.