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Artificial Neural Networks and Machine Learning – ICANN 2021

Overview of attention for book
Artificial Neural Networks and Machine Learning – ICANN 2021
Springer International Publishing
Attention for Chapter: Jacobian Regularization for Mitigating Universal Adversarial Perturbations
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

twitter
7 X users

Readers on

mendeley
8 Mendeley
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Chapter title
Jacobian Regularization for Mitigating Universal Adversarial Perturbations
Book title
Artificial Neural Networks and Machine Learning – ICANN 2021
Published in
arXiv, September 2021
DOI 10.1007/978-3-030-86380-7_17
Book ISBNs
978-3-03-086379-1, 978-3-03-086380-7
Authors

Kenneth T. Co, David Martinez Rego, Emil C. Lupu, Co, Kenneth T., Rego, David Martinez, Lupu, Emil C.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 25%
Student > Ph. D. Student 2 25%
Student > Doctoral Student 1 13%
Librarian 1 13%
Researcher 1 13%
Other 0 0%
Unknown 1 13%
Readers by discipline Count As %
Computer Science 3 38%
Engineering 2 25%
Physics and Astronomy 1 13%
Agricultural and Biological Sciences 1 13%
Unknown 1 13%
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 14 September 2021.
All research outputs
#7,758,831
of 24,093,053 outputs
Outputs from arXiv
#168,062
of 1,020,419 outputs
Outputs of similar age
#151,099
of 417,343 outputs
Outputs of similar age from arXiv
#5,886
of 34,065 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 1,020,419 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done well, scoring higher than 82% 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 417,343 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 63% of its contemporaries.
We're also able to compare this research output to 34,065 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.