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Efficient Evolutionary Search of Attention Convolutional Networks via Sampled Training and Node Inheritance

Overview of attention for article published in IEEE Transactions on Evolutionary Computation, November 2020
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (62nd percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

twitter
1 X user
patent
5 patents

Readers on

mendeley
33 Mendeley
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Article details
Title
Efficient Evolutionary Search of Attention Convolutional Networks via Sampled Training and Node Inheritance
Published in
IEEE Transactions on Evolutionary Computation, November 2020
DOI 10.1109/tevc.2020.3040272
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 33 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 33 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Master 4 12%
Student > Bachelor 3 9%
Student > Ph. D. Student 3 9%
Student > Postgraduate 2 6%
Lecturer > Senior Lecturer 1 3%
Other 2 6%
Unknown 18 55%
Readers by discipline
Readers by discipline Count As %
Computer Science 12 36%
Engineering 3 9%
Linguistics 1 3%
Unknown 17 52%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 12 November 2024.
All research outputs
#9,334,445
of 28,118,768 outputs
Outputs from IEEE Transactions on Evolutionary Computation
#97
of 733 outputs
Outputs of similar age
#199,010
of 536,079 outputs
Outputs of similar age from IEEE Transactions on Evolutionary Computation
#1
of 5 outputs
Altmetric has tracked 28,118,768 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 733 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done well, scoring higher than 84% 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 536,079 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 62% of its contemporaries.
We're also able to compare this research output to 5 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