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Improving latent variable descriptiveness by modelling rather than ad-hoc factors

Overview of attention for article published in Machine Learning, July 2019
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1 X user

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mendeley
13 Mendeley
Title
Improving latent variable descriptiveness by modelling rather than ad-hoc factors
Published in
Machine Learning, July 2019
DOI 10.1007/s10994-019-05830-1
Authors

Alex Mansbridge, Roberto Fierimonte, Ilya Feige, David Barber

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 readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 15%
Student > Ph. D. Student 2 15%
Student > Doctoral Student 1 8%
Student > Bachelor 1 8%
Professor 1 8%
Other 2 15%
Unknown 4 31%
Readers by discipline Count As %
Computer Science 3 23%
Linguistics 1 8%
Nursing and Health Professions 1 8%
Economics, Econometrics and Finance 1 8%
Physics and Astronomy 1 8%
Other 1 8%
Unknown 5 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 21 August 2019.
All research outputs
#20,577,025
of 23,154,520 outputs
Outputs from Machine Learning
#963
of 981 outputs
Outputs of similar age
#294,513
of 346,252 outputs
Outputs of similar age from Machine Learning
#11
of 11 outputs
Altmetric has tracked 23,154,520 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 981 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 346,252 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.