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Simple statistical gradient-following algorithms for connectionist reinforcement learning

Overview of attention for article published in Machine Learning, May 1992
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#14 of 1,266)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

news
3 news outlets
blogs
1 blog
policy
1 policy source
twitter
5 X users
patent
39 patents
wikipedia
6 Wikipedia pages

Citations

dimensions_citation
3712 Dimensions

Readers on

mendeley
929 Mendeley
citeulike
7 CiteULike
Title
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Published in
Machine Learning, May 1992
DOI 10.1007/bf00992696
Authors

Ronald J. Williams

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 929 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 13 1%
Switzerland 6 <1%
United Kingdom 5 <1%
France 4 <1%
Japan 4 <1%
China 3 <1%
Belgium 3 <1%
Canada 3 <1%
Czechia 2 <1%
Other 15 2%
Unknown 871 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 248 27%
Student > Master 163 18%
Researcher 122 13%
Student > Bachelor 67 7%
Student > Doctoral Student 43 5%
Other 131 14%
Unknown 155 17%
Readers by discipline Count As %
Computer Science 427 46%
Engineering 136 15%
Agricultural and Biological Sciences 40 4%
Neuroscience 25 3%
Mathematics 23 2%
Other 93 10%
Unknown 185 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 42. 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 12 March 2024.
All research outputs
#1,008,979
of 25,837,817 outputs
Outputs from Machine Learning
#14
of 1,266 outputs
Outputs of similar age
#139
of 18,347 outputs
Outputs of similar age from Machine Learning
#2
of 10 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,266 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 98% 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 18,347 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 8 of them.