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Hierarchical Sparse Coding of Objects in Deep Convolutional Neural Networks

Overview of attention for article published in Frontiers in Computational Neuroscience, December 2020
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3 X users

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15 Mendeley
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Title
Hierarchical Sparse Coding of Objects in Deep Convolutional Neural Networks
Published in
Frontiers in Computational Neuroscience, December 2020
DOI 10.3389/fncom.2020.578158
Pubmed ID
Authors

Xingyu Liu, Zonglei Zhen, Jia Liu

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X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 20%
Professor > Associate Professor 2 13%
Student > Bachelor 2 13%
Unspecified 1 7%
Researcher 1 7%
Other 1 7%
Unknown 5 33%
Readers by discipline Count As %
Neuroscience 3 20%
Engineering 2 13%
Psychology 1 7%
Physics and Astronomy 1 7%
Unspecified 1 7%
Other 0 0%
Unknown 7 47%
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 23 December 2020.
All research outputs
#15,660,371
of 23,270,775 outputs
Outputs from Frontiers in Computational Neuroscience
#882
of 1,369 outputs
Outputs of similar age
#306,424
of 508,468 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#20
of 25 outputs
Altmetric has tracked 23,270,775 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,369 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one is in the 28th percentile – i.e., 28% 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 508,468 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.