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On the Importance of Normalisation Layers in Deep Learning with Piecewise Linear Activation Units

Overview of attention for article published in arXiv, March 2016
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  • High Attention Score compared to outputs of the same age and source (85th percentile)

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Article details
Title
On the Importance of Normalisation Layers in Deep Learning with Piecewise Linear Activation Units
Published in
arXiv, March 2016
DOI 10.1109/wacv.2016.7477624
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Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 18 November 2022.
All research outputs
#10,799,052
of 33,695,692 outputs
Outputs from arXiv
#239,745
of 1,481,422 outputs
Outputs of similar age
#112,495
of 334,450 outputs
Outputs of similar age from arXiv
#1,624
of 11,648 outputs
Altmetric has tracked 33,695,692 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 1,481,422 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done well, scoring higher than 83% 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 334,450 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 11,648 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.