| Title |
Optimizing non-decomposable measures with deep networks
|
|---|---|
| Published in |
Machine Learning, July 2018
|
| DOI | 10.1007/s10994-018-5736-y |
| Authors |
Amartya Sanyal, Pawan Kumar, Purushottam Kar, Sanjay Chawla, Fabrizio Sebastiani |
X Demographics
The data shown below were collected from the profiles of 19 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 | 4 | 21% |
| India | 1 | 5% |
| France | 1 | 5% |
| Switzerland | 1 | 5% |
| United Kingdom | 1 | 5% |
| Germany | 1 | 5% |
| Unknown | 10 | 53% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 14 | 74% |
| Scientists | 5 | 26% |
Mendeley readers
The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 40 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 30% |
| Student > Master | 6 | 15% |
| Researcher | 5 | 13% |
| Student > Doctoral Student | 3 | 8% |
| Other | 2 | 5% |
| Other | 3 | 8% |
| Unknown | 9 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 22 | 55% |
| Engineering | 2 | 5% |
| Medicine and Dentistry | 2 | 5% |
| Psychology | 1 | 3% |
| Biochemistry, Genetics and Molecular Biology | 1 | 3% |
| Other | 2 | 5% |
| Unknown | 10 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 26 May 2019.
All research outputs
#3,686,342
of 24,998,746 outputs
Outputs from Machine Learning
#93
of 1,152 outputs
Outputs of similar age
#69,556
of 333,883 outputs
Outputs of similar age from Machine Learning
#5
of 13 outputs
Altmetric has tracked 24,998,746 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,152 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done particularly well, scoring higher than 92% 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 333,883 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.