The world's first "one million citation" scholar is born. Yoshua Bengio is deified, and Geoffrey Hinton and He Kaiming closely follow.

Yoshua Bengio has become the first person on Google Scholar with over 1 million paper citations!

Query time: October 25, 2025, 13:04

Meanwhile, the citation count of the godfather of artificial intelligence, Geoffrey Hinton, has also reached 970,000!

He is expected to become the second person globally with over 1 million paper citations.

Query time: October 25, 2025, 13:04

As one of the three giants of deep learning and a Turing Award winner, Bengio not only has the highest citation count in the computer field but is also number one in the world.

As early as 2018, he won the title of the top computer researcher with the "highest single-day citation count" and received the Turing Award in the same year as Geoffrey Hinton and Yann LeCun.

Ranking platform: AD Scientific Index

As a pioneer in the fields of deep learning and natural language, Yoshua Bengio's citation growth curve began to explode after he won the Turing Award in 2018.

And the citation growth curve of the elder Hinton movingly shows how he survived the cold "AI winter".

Yann LeCun, also one of the three Turing Award giants, also has over 430,000 citations.

But it is still much lower than that of He Kaiming, Ilya Sutskever and others.

Why has there been an explosion in AI paper citations?

If carefully examined, among the global top 10 highly cited researchers, four are computer experts, and all of these experts can be said to be in the AI field!

The four experts are: Yoshua Bengio, Geoffrey Hinton, He Kaiming, and Ilya Sutskever.

Among them:

Yoshua Bengio and Geoffrey Hinton are pioneers in the field of deep learning;

Geoffrey Hinton and Ilya Sutskever have a teacher-student relationship. There's no need to say more about Hinton; Ilya is the creator of OpenAI and ChatGPT;

He Kaiming is the father of the deep residual network (ResNets).

The explosive growth of paper citations in the field of deep learning coincides with the AI era we are in.

In 2012, AlexNet significantly outperformed others in ImageNet and for the first time wrote large-scale GPU training into the paper methodology, which is regarded as the "tipping point" of deep learning.

From backpropagation in 1986 to the review "Deep Learning" by LeCun, Bengio, and Hinton published in Nature in 2015, and then to the review on "representation learning" by Bengio's team, the papers of these experts have become the standard theories and background citation sources for later paper writing.

And the Transformer and large-model AI have brought about a second explosion!

In 2017, the Transformer was proposed. In 2018, BERT proved that the pre-training/fine-tuning paradigm is applicable to natural language processing. Subsequently, multimodal and generative AI have blossomed in an all-round way.

Almost all new papers will trace back to the earlier literature chain of neural networks and representation learning.

When AI becomes the new global growth point, the total number of AI papers has increased significantly, amplifying the "citation parent set".

The AI Index points out that from 2010 to 2022, the total number of global AI papers has almost tripled (from about 88,000 to over 240,000).

By 2023, the proportion of AI in computer science papers has also increased from 21.6% in 2013 to 41.8%.

It can be said that almost half of the papers in the field of computer science are related to AI, and AI is inseparable from deep learning.

Therefore, the larger the research parent set, the faster the cumulative citation growth of the founders.

This trend can also be confirmed by the submissions of top conference papers in the AI field.

ICLR 2024: A total of 7,262 submissions, 2,260 accepted, an increase of 2,324 submissions compared to 2023.

NeurIPS 2024: 17,491 total submissions (15,671 for the main conference + 1,820 for datasets/benchmarks), 4,497 accepted.

CVPR 2024: 11,532 submissions, 2,719 accepted, with an acceptance rate of 23.6%.

AI has become the main theme of this era.

And the core papers in the AI field have also become the main theme of academia in this era.

Highly cited authors in the AI field

Yoshua Bengio

Yoshua Bengio

Yoshua Bengio is a globally recognized top expert in the field of artificial intelligence, and he is well-known for his pioneering research in deep learning.

Bengio won the Turing Award in 2018 together with Geoffrey Hinton and Yann LeCun and is regarded as one of the most important "three giants" promoting the development of deep learning.

Bengio is a full professor at the University of Montreal, the founder and scientific advisor of Mila (Quebec Artificial Intelligence Institute). He is also a senior researcher at CIFAR, a special advisor at IVADO, and a CIFAR AI Chair Professor in Canada.

In 2019, Bengio won the Killam Award.

In 2022, he became one of the computer scientists with the highest global citation count in the h-index.

Early life and educational background

Yoshua Bengio was born in Paris, France, in 1964. He spent his teenage years in Montreal, Canada.

Bengio comes from a Jewish family. His parents immigrated from Morocco to France and then moved to Canada.

He obtained a bachelor's degree in engineering, a master's degree in computer science, and a doctorate in computer science from McGill University.

In 1991, after graduating with a doctorate from McGill University, Bengio served as a postdoctoral researcher at MIT and AT&T Bell Laboratories.

Bengio is a fellow of the Royal Society, a fellow of the Royal Society of Canada, a Knight of the Legion of Honour of France, and an Officer of the Order of Canada.

In 2023, he was appointed a member of the United Nations Scientific Advisory Committee to provide independent advice on scientific and technological breakthroughs.

Out of deep concern about the social impact of artificial intelligence, Bengio participated in drafting the "Montreal Declaration for the Responsible Development of Artificial Intelligence" and continues to advocate for reducing the potential catastrophic risks that future artificial intelligence systems may bring.

He Kaiming

The total citation count of He Kaiming's papers has exceeded 750,000.

Currently, he is a tenured associate professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT) and also serves as a distinguished scientist at Google DeepMind. His research interests are in computer vision and deep learning.

He worked as a research scientist at the Facebook AI Research (FAIR) from 2016 to 2024 and as a researcher at the Microsoft Research Asia (MSRA) from 2011 to 2016. He obtained a doctorate from the Chinese University of Hong Kong in 2011 and a bachelor's degree from Tsinghua University in 2007.

According to an article in Nature, his representative work, the deep residual network (ResNets), is the most cited paper in the 21st century.

The residual connection structure has been widely used in various advanced deep learning models today, including Transformer (such as ChatGPT), AlphaGo Zero, AlphaFold, and almost all generative AI models.

In addition, his research in the fields of visual perception (such as Faster R-CNN, Mask R-CNN) and self-supervised learning (such as MoCo, MAE) is also well-known.

He has won many important awards, including the 2025 ICCV Helmholtz Prize (Award for the Test of Time), the 2018 PAMI Young Researcher Award, the Best Paper Awards at ICCV 2017, CVPR 2016, and CVPR 2009, the Best Student Paper Award at ICCV 2017, the Best Paper Honorable Mentions at CVPR 2021 and ECCV 2018, and the 2021 ICCV Everingham Prize.

Ilya Sutskever

The total citation count of Ilya Sutskever's papers has exceeded 700,000.