Google Scholar reveals its most influential papers for 2019

Google Scholar reveals its most influential papers for 2019

This article was first published by Nature Index.

The just-released Google Scholar ranking of most highly cited publications reveal the tremendous rise in interest surrounding artificial intelligence (AI) research.

Of the five most highly-cited papers in Nature – which itself is ranked by Google Scholar as the most influential journal – three are related to AI, and one has raked in more than 16,000 citations.

The publication accompanying one of the top AI conferences in the world – the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) – makes its debut in the top 10 journals this year, up from 20th place in 2018. One of its papers has clocked 25,256 citations in the past three years.

Tracking citation information for almost 400 million academic papers and other scholarly literature, Google Scholar is the largest database in the world of its kind, and aims to measure the "visibility and influence" of recent publications.

The 2019 Google Scholar Metrics ranking, which is freely accessible online, tracks papers published between 2014 and 2018, and includes citations from all articles that were indexed in Google Scholar as of July 2019.

Below is a selection of its most highly cited articles published by the world's most influential journals.

1. "Deep Residual Learning for Image Recognition" (2016) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 25,256 citations

Of the 100 top-ranked journals this year, five are AI conference publications. This particular journal, which made a huge leap in the rankings this year, has three articles with more than 10,000 citations each – a feat not matched by any other journal.

As Synced's Fangyu Cai points out, "It should come as no surprise … that AI conferences are publishing so prodigiously – in recent years they have evolved from low-key academic gatherings into extravagant multimedia events attracting thousands and serving as showcases for major innovations and breakthroughs in AI research, development, and deployment."

This particular article was written by a research team from Microsoft – a company that achieved a significant increase in high-quality research output in 2018, as tracked by the Nature Index.

2. "Deep learning" (2015) Nature 16,750 citations

This paper stands out not just because of its high number of citations, but because there was a difference of more than 10,000 between its citation count and the second most-cited Nature paper in the 2019 Google Scholar Metrics report.

Authored by 2018 Turing Award winners, Yann LeCun, Yoshua Bengio and Geoffrey Hinton – known collectively as the 'Godfathers of AI' – the paper is a seminal review of the potential of the AI technologies.

3. "Going Deeper with Convolutions" (2015) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 14,424 citations

This paper by Google AI researchers describes their new object-detection system, GoogLeNet, built using a deep neural network system codenamed Inception.

It received top marks in the 2014 ImageNet Large Scale Visual Recognition Challenge – an international computer vision competition.

In 2018, Google’s parent company, Alphabet, was the sixth most prolific corporate entity in high-quality research output in the Nature Index.

4. "Fully Convolutional Networks for Semantic Segmentation" (2015) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 10,153 citations

A team from the University of California, Berkeley was responsible for this highly influential AI paper, which describes a state-of-the-art approach to building AI systems that can identify objects in images.

This particular type of model, semantic segmentation, can be used to count the number of objects in a single image, which has great potential for technologies such as self-driving cars and robotics.

5. "Prevalence of Childhood and Adult Obesity in the United States, 2011-2012" (2014) JAMA 8,057 citations

No non-AI-related paper received more than 10,000 citations in the 2019 Google Scholar Metrics, but this investigation by a team from the US Centers for Disease Control and Prevention came close.

At the time of its release, the study provided the most up-to-date national estimates of childhood obesity in the United States and found that more than one-third of adults and 17% of youth were obese.

It did conclude, however, that there had been no significant changes in obesity prevalence in youth or adults between 2003-2004 and 2011-2012.

6. "Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013" (2014) Lancet 7,371 citations

With almost 200 authors from more than 100 institutions, this paper signifies an enormous undertaking to investigate the global prevalence of obesity.

The study found that the proportion of adults in the world that were overweight or obese had increased between 1980 and 2013, particularly in developed countries, and identified obesity as a major global health challenge.

The second most-cited Lancet paper had 2,459 citations, making this paper an anomaly for the medical journal.

7. "Observation of Gravitational Waves from a Binary Black Hole Merger" (2016) Physical Review Letters 6,009 citations

This study stands out among the rest in this list as one that gained significant media attention and engagement from the general public, making an impact both inside and outside academia.Through this paper, a team of physicists from the Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) described the first direct observation of gravitational waves – a feat that took a century to achieve following Einstein’s prediction of these elusive ripples in space-time.

The study got 2.5 times more citations than the second most-cited paper in Physical Review Letters, also from LIGO, which in 2017 announced the first direct observation of a merger between two neutron stars.

See the full 2019 Google Scholar Metrics.



Phone: +1-628-265-6690


© 2020 Neuralens Technologies, Inc.