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Applications and Techniques for Fast Machine Learning in Science

Overview of attention for article published in arXiv, April 2022
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
2 news outlets
twitter
205 X users

Citations

dimensions_citation
43 Dimensions

Readers on

mendeley
99 Mendeley
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Title
Applications and Techniques for Fast Machine Learning in Science
Published in
arXiv, April 2022
DOI 10.3389/fdata.2022.787421
Pubmed ID
Authors

Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini, Thea Aarrestad, Steffen Bähr, Jürgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomás E. Müller Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Dongning Guo, Kyle J. Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belina von Krosigk, Shen Wang, Thomas K. Warburton

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X Demographics

X Demographics

The data shown below were collected from the profiles of 205 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 99 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 99 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 16%
Researcher 13 13%
Student > Master 5 5%
Unspecified 4 4%
Student > Doctoral Student 4 4%
Other 18 18%
Unknown 39 39%
Readers by discipline Count As %
Computer Science 12 12%
Engineering 11 11%
Physics and Astronomy 8 8%
Unspecified 7 7%
Chemistry 3 3%
Other 13 13%
Unknown 45 45%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 148. 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 23 December 2023.
All research outputs
#299,581
of 26,614,447 outputs
Outputs from arXiv
#3,284
of 1,014,885 outputs
Outputs of similar age
#8,327
of 455,768 outputs
Outputs of similar age from arXiv
#84
of 28,281 outputs
Altmetric has tracked 26,614,447 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,014,885 research outputs from this source. They receive a mean Attention Score of 4.2. This one has done particularly well, scoring higher than 99% 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 455,768 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 28,281 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.