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Differentiable programming for image processing and deep learning in halide

Overview of attention for article published in ACM Transactions on Graphics, August 2018
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About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
102 Mendeley
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Title
Differentiable programming for image processing and deep learning in halide
Published in
ACM Transactions on Graphics, August 2018
DOI 10.1145/3197517.3201383
Authors

Tzu-Mao Li, Michaël Gharbi, Andrew Adams, Frédo Durand, Jonathan Ragan-Kelley

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 102 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 21%
Student > Master 19 19%
Researcher 17 17%
Student > Bachelor 9 9%
Other 6 6%
Other 13 13%
Unknown 17 17%
Readers by discipline Count As %
Computer Science 61 60%
Engineering 11 11%
Mathematics 3 3%
Physics and Astronomy 2 2%
Psychology 1 <1%
Other 2 2%
Unknown 22 22%

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 March 2019.
All research outputs
#9,169,733
of 14,574,989 outputs
Outputs from ACM Transactions on Graphics
#1,517
of 1,700 outputs
Outputs of similar age
#165,741
of 276,769 outputs
Outputs of similar age from ACM Transactions on Graphics
#29
of 39 outputs
Altmetric has tracked 14,574,989 research outputs across all sources so far. This one is in the 24th percentile – i.e., 24% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,700 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.4. This one is in the 4th percentile – i.e., 4% 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 276,769 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.