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A flexible probabilistic framework for large-margin mixture of experts

Overview of attention for article published in Machine Learning, June 2019
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Mentioned by

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1 X user

Citations

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5 Dimensions

Readers on

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9 Mendeley
Title
A flexible probabilistic framework for large-margin mixture of experts
Published in
Machine Learning, June 2019
DOI 10.1007/s10994-019-05811-4
Authors

Archit Sharma, Siddhartha Saxena, Piyush Rai

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 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 33%
Student > Doctoral Student 2 22%
Student > Bachelor 1 11%
Unknown 3 33%
Readers by discipline Count As %
Computer Science 3 33%
Mathematics 1 11%
Economics, Econometrics and Finance 1 11%
Decision Sciences 1 11%
Unknown 3 33%
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 19 June 2019.
All research outputs
#18,684,243
of 23,150,406 outputs
Outputs from Machine Learning
#897
of 981 outputs
Outputs of similar age
#263,355
of 352,677 outputs
Outputs of similar age from Machine Learning
#10
of 11 outputs
Altmetric has tracked 23,150,406 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% 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 352,677 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% 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.