Google Scholar reveals its most influential papers for 2019
Of the five most exceptionally refered to papers in Nature – which itself is positioned by Google Scholar as the most compelling diary – three are identified with AI, and one has rounded up in excess of 16,000 references.
The production going with one of the tops AI gatherings on the planet – the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) – causes its presentation in the main 10 diaries this year, to up from the twentieth spot in 2018. One of its papers has checked 25,256 references in the previous three years.
Following reference data for just about 400 million scholastic papers and other academic writing, Google Scholar is the biggest database in the realm of its sort and expects to gauge the "perceivability and impact" of late productions.
The 2019 Google Scholar Metrics positioning, which is openly available on the web, tracks papers distributed somewhere in the range of 2014 and 2018 and incorporates references from all articles that were listed in Google Scholar starting in July 2019.
The following is a choice of its most profoundly refered to articles distributed by the world's most compelling diaries.
1. "Profound Residual Learning for Image Recognition" (2016)
Procedures of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
25,256 references
Of the 100 top-positioned diaries this year, five are AI meeting productions. This specific diary, which made a gigantic jump in the rankings this year, has three articles with in excess of 10,000 references each – an accomplishment not coordinated by some other diary.
As Synced's Fangyu Cai calls attention to, "It should not shock anyone … that AI meetings are distributing so colossally – as of late they have advanced from serene scholarly social occasions into lavish media occasions pulling in thousands and filling in as grandstands for significant developments and leaps forward in AI research, improvement, and arrangement."
This specific article was composed by an examination group from Microsoft – an organization that accomplished a noteworthy increment in top-notch research yield in 2018, as followed by the Nature Index.
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2. "Profound learning" (2015)
Nature
16,750 references
This paper stands apart not as a result of its high number of references, but since there was a distinction of more than 10,000 between its reference tally and the second most-refered to Nature paper in the 2019 Google Scholar Metrics report.
Composed by 2018 Turing Award champs, Yann LeCun, Yoshua Bengio, and Geoffrey Hinton – referred to aggregately as the 'Back up parents of AI' – the paper is a fundamental survey of the capability of the AI advances.
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3. "Going Deeper with Convolutions" (2015)
Procedures of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
14,424 references
This paper by Google AI specialists portrays their new item discovery framework, GoogLeNet, assembled utilizing a profound neural system framework codenamed Inception.
It got good grades in the 2014 ImageNet Large Scale Visual Recognition Challenge – a universal PC vision rivalry.
In 2018, Google's parent organization, Alphabet, was the 6th most productive corporate element in top-notch research yield in the Nature Index.
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4. "Completely Convolutional Networks for Semantic Segmentation" (2015)
Procedures of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
10,153 references
A group from the University of California, Berkeley was liable for this profoundly powerful AI paper, which depicts a cutting edge way to deal with structure AI frameworks that can recognize protests in pictures.
This specific sort of model, semantic division, can be utilized to include the number of articles in a solitary picture, which has incredible potential for advances, for example, self-driving vehicles and apply autonomy.
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5. "Pervasiveness of Childhood and Adult Obesity in the United States, 2011-2012" (2014)
JAMA
8,057 references
No non-AI-related paper got in excess of 10,000 references in the 2019 Google Scholar Metrics, yet this examination by a group from the US Centers for Disease Control and Prevention approached.
At the hour of its discharge, the examination gave the most modern national appraisals of youth corpulence in the United States and found that more than 33% of grown-ups and 17% of youth were large.
It concluded, be that as it may, that there had been no huge changes in stoutness commonness in youth or grown-ups between 2003-2004 and 2011-2012.
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6. "Worldwide, local, and national predominance of overweight and stoutness in kids and grown-ups during 1980–2013: a precise examination for the Global Burden of Disease Study 2013" (2014)
Lancet
7,371 references
With very nearly 200 creators from in excess of 100 organizations, this paper means a tremendous endeavor to examine the worldwide pervasiveness of stoutness.
The examination found that the extent of grown-ups on the planet that were overweight or fat had expanded somewhere in the range of 1980 and 2013, especially in created nations, and recognized heftiness as a significant worldwide wellbeing challenge.
The second most-refered to Lancet paper had 2,459 references, making this paper an oddity for the medicinal diary.
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7. "Perception of Gravitational Waves from a Binary Black Hole Merger" (2016)
Physical Review Letters
6,009 references
This investigation stands apart among the rest in this rundown as one that increased noteworthy media consideration and commitment from the overall population, having an effect both inside and outside the scholarly community.
Through this paper, a group of physicists from the Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) depicted the principal direct perception of gravitational waves – an accomplishment that took a century to accomplish following Einstein's expectation of these slippery waves in space-time.
The examination got 2.5 occasions a larger number of references than the second most-refered to paper in Physical Review Letters, additionally from LIGO, which in 2017 declared the primary direct perception of a merger between two neutron stars.
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