Artificial Neural Networks and Machine Learning – ICANN 2021
Springer International Publishing
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X Demographics
Mendeley readers
Attention Score in Context
| 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
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 14% |
| Japan | 1 | 14% |
| Unknown | 5 | 71% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 100% |
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
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.