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Approximate Fisher Information Matrix to Characterize the Training of Deep Neural Networks

Overview of attention for article published in IEEE Transactions on Software Engineering, October 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

twitter
1 X user
patent
2 patents

Readers on

mendeley
69 Mendeley
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Article details
Title
Approximate Fisher Information Matrix to Characterize the Training of Deep Neural Networks
Published in
IEEE Transactions on Software Engineering, October 2018
DOI 10.1109/tpami.2018.2876413
Pubmed ID
Authors

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Timeline Attention over time Attention Score history
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Activity
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X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 69 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 18 26%
Student > Master 11 16%
Researcher 8 12%
Other 3 4%
Student > Bachelor 3 4%
Other 9 13%
Unknown 17 25%
Readers by discipline
Readers by discipline Count As %
Computer Science 26 38%
Engineering 15 22%
Physics and Astronomy 2 3%
Neuroscience 2 3%
Mathematics 1 1%
Other 4 6%
Unknown 19 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 04 December 2024.
All research outputs
#7,302,629
of 34,376,207 outputs
Outputs from IEEE Transactions on Software Engineering
#1,806
of 7,914 outputs
Outputs of similar age
#103,129
of 391,116 outputs
Outputs of similar age from IEEE Transactions on Software Engineering
#19
of 73 outputs
Altmetric has tracked 34,376,207 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,914 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one has done well, scoring higher than 76% 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 391,116 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 73% of its contemporaries.
We're also able to compare this research output to 73 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.