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Deep Learning: Concepts and Architectures

Overview of attention for book
Cover of 'Deep Learning: Concepts and Architectures'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Deep Learning Architectures
  3. Altmetric Badge
    Chapter 2 Theoretical Characterization of Deep Neural Networks
  4. Altmetric Badge
    Chapter 3 Scaling Analysis of Specialized Tensor Processing Architectures for Deep Learning Models
  5. Altmetric Badge
    Chapter 4 Assessment of Autoencoder Architectures for Data Representation
  6. Altmetric Badge
    Chapter 5 The Encoder-Decoder Framework and Its Applications
  7. Altmetric Badge
    Chapter 6 Deep Learning for Learning Graph Representations
  8. Altmetric Badge
    Chapter 7 Deep Neural Networks for Corrupted Labels
  9. Altmetric Badge
    Chapter 8 Constructing a Convolutional Neural Network with a Suitable Capacity for a Semantic Segmentation Task
  10. Altmetric Badge
    Chapter 9 Using Convolutional Neural Networks to Forecast Sporting Event Results
  11. Altmetric Badge
    Chapter 10 Heterogeneous Computing System for Deep Learning
  12. Altmetric Badge
    Chapter 11 Progress in Neural Network Based Statistical Language Modeling
Attention for Chapter 6: Deep Learning for Learning Graph Representations
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

twitter
24 X users

Citations

dimensions_citation
21 Dimensions

Readers on

mendeley
108 Mendeley
Summary X Dimensions citations
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
Deep Learning for Learning Graph Representations
Chapter number 6
Book title
Deep Learning: Concepts and Architectures
Published in
arXiv, January 2020
DOI 10.1007/978-3-030-31756-0_6
Book ISBNs
978-3-03-031755-3, 978-3-03-031756-0
Authors

Wenwu Zhu, Xin Wang, Peng Cui, Zhu, Wenwu, Wang, Xin, Cui, Peng

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 24 X users 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 108 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 108 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 18 17%
Student > Ph. D. Student 15 14%
Researcher 14 13%
Student > Bachelor 10 9%
Student > Postgraduate 6 6%
Other 15 14%
Unknown 30 28%
Readers by discipline Count As %
Computer Science 56 52%
Engineering 6 6%
Medicine and Dentistry 3 3%
Mathematics 2 2%
Physics and Astronomy 2 2%
Other 5 5%
Unknown 34 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 08 January 2020.
All research outputs
#3,502,716
of 23,885,338 outputs
Outputs from arXiv
#66,838
of 1,007,682 outputs
Outputs of similar age
#83,480
of 462,407 outputs
Outputs of similar age from arXiv
#2,237
of 27,709 outputs
Altmetric has tracked 23,885,338 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,007,682 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done particularly well, scoring higher than 93% 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 462,407 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 81% of its contemporaries.
We're also able to compare this research output to 27,709 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.

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