H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 113 Citations 245,103 264 World Ranking 76 National Ranking 46

Research.com Recognitions

Awards & Achievements

2020 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to deep learning, neural networks, and image recognition, including the introduction of convolutional neural networks.

2018 - A. M. Turing Award For conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.

2017 - Member of the National Academy of Engineering For developing convolutional neural networks and their applications in computer vision and other areas of artificial intelligence.

2014 - Neural Networks Pioneer Award, IEEE Computational Intelligence Society

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Operating system

Yann LeCun mainly focuses on Artificial intelligence, Pattern recognition, Machine learning, Deep learning and Computer vision. Many of his studies involve connections with topics such as Speech recognition and Artificial intelligence. His Speech recognition study integrates concerns from other disciplines, such as Backpropagation and Digit recognition.

The various areas that Yann LeCun examines in his Backpropagation study include Representation and Computational model. His Pattern recognition research incorporates themes from Optical flow and Word error rate. His work deals with themes such as Bag-of-words model, Natural language processing, Geometric data analysis and Character, which intersect with Deep learning.

His most cited work include:

  • Deep learning (28315 citations)
  • Deep learning (28315 citations)
  • Gradient-based learning applied to document recognition (26443 citations)

What are the main themes of his work throughout his whole career to date?

Yann LeCun spends much of his time researching Artificial intelligence, Pattern recognition, Machine learning, Artificial neural network and Computer vision. Artificial intelligence and Speech recognition are frequently intertwined in his study. Yann LeCun combines subjects such as Pixel, Invariant and Pooling with his study of Pattern recognition.

His Computer vision study incorporates themes from Classifier and Robot. In his study, which falls under the umbrella issue of Feature extraction, Object detection is strongly linked to Cognitive neuroscience of visual object recognition. Yann LeCun regularly links together related areas like Algorithm in his Deep learning studies.

He most often published in these fields:

  • Artificial intelligence (74.47%)
  • Pattern recognition (28.46%)
  • Machine learning (21.28%)

What were the highlights of his more recent work (between 2016-2021)?

  • Artificial intelligence (74.47%)
  • Machine learning (21.28%)
  • Algorithm (11.70%)

In recent papers he was focusing on the following fields of study:

The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Algorithm, Artificial neural network and Deep learning. His research links Pattern recognition with Artificial intelligence. In general Machine learning study, his work on Latent variable often relates to the realm of Space, thereby connecting several areas of interest.

He has researched Algorithm in several fields, including Redundancy, Function, Representation and Generator. The study incorporates disciplines such as Generalization, Mathematical optimization and Nested loop join in addition to Artificial neural network. His work on MNIST database as part of general Deep learning study is frequently linked to Spin glass, therefore connecting diverse disciplines of science.

Between 2016 and 2021, his most popular works were:

  • Geometric Deep Learning: Going beyond Euclidean data (1319 citations)
  • A Closer Look at Spatiotemporal Convolutions for Action Recognition (856 citations)
  • Very deep convolutional networks for text classification (373 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Operating system

Yann LeCun mainly investigates Artificial intelligence, Artificial neural network, Machine learning, Deep learning and Convolutional neural network. His Artificial intelligence research includes elements of Generator, Theoretical computer science and Pattern recognition. His Pattern recognition research includes themes of Object detection and Block.

His biological study spans a wide range of topics, including Generalization, Mathematical optimization and Maxima and minima. His study in Deep learning is interdisciplinary in nature, drawing from both Semi-supervised learning, Computer hardware, Discriminator, Function and Software. His Convolutional neural network research is multidisciplinary, incorporating perspectives in Optical flow, Segmentation and RGB color model.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Deep learning

Yann LeCun;Yann LeCun;Yoshua Bengio;Geoffrey Hinton;Geoffrey Hinton.
Nature (2015)

30518 Citations

Gradient-based learning applied to document recognition

Yann Lecun;Leon Bottou;Leon Bottou;Yoshua Bengio;Yoshua Bengio;Yoshua Bengio;Patrick Haffner;Patrick Haffner.
Proceedings of the IEEE (1998)

30145 Citations

Backpropagation applied to handwritten zip code recognition

Y. LeCun;B. Boser;J. S. Denker;D. Henderson.
Neural Computation (1989)

7162 Citations

Convolutional networks for images, speech, and time series

Yann LeCun;Yoshua Bengio;Yoshua Bengio;Yoshua Bengio.
The handbook of brain theory and neural networks (1998)

3758 Citations

Efficient BackProp

Yann LeCun;Léon Bottou;Genevieve B. Orr;Klaus-Robert Müller.
neural information processing systems (1998)

3747 Citations

Handwritten Digit Recognition with a Back-Propagation Network

Yann LeCun;Bernhard E. Boser;John S. Denker;John S. Denker;Donnie Henderson.
neural information processing systems (1989)

3571 Citations

Optimal Brain Damage

Yann LeCun;John S. Denker;Sara A. Solla.
neural information processing systems (1989)

3571 Citations

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

Pierre Sermanet;David Eigen;Xiang Zhang;Michael Mathieu.
international conference on learning representations (2014)

2887 Citations

Learning Hierarchical Features for Scene Labeling

C. Farabet;C. Couprie;L. Najman;Y. LeCun.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2013)

2467 Citations

Learning a similarity metric discriminatively, with application to face verification

S. Chopra;R. Hadsell;Y. LeCun.
computer vision and pattern recognition (2005)

2443 Citations

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Best Scientists Citing Yann LeCun

Yoshua Bengio

Yoshua Bengio

University of Montreal

Publications: 240

Luc Van Gool

Luc Van Gool

ETH Zurich

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Xiaogang Wang

Xiaogang Wang

Chinese University of Hong Kong

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Qi Tian

Qi Tian

Huawei Technologies (China)

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Shuicheng Yan

Shuicheng Yan

National University of Singapore

Publications: 115

Dacheng Tao

Dacheng Tao

University of Sydney

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Klaus-Robert Müller

Klaus-Robert Müller

Technical University of Berlin

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Trevor Darrell

Trevor Darrell

University of California, Berkeley

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Kaushik Roy

Kaushik Roy

Purdue University West Lafayette

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Andrew Zisserman

Andrew Zisserman

University of Oxford

Publications: 98

Alan L. Yuille

Alan L. Yuille

Johns Hopkins University

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Ross Girshick

Ross Girshick

Facebook (United States)

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Xiaoou Tang

Xiaoou Tang

Chinese University of Hong Kong

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U. Rajendra Acharya

U. Rajendra Acharya

Ngee Ann Polytechnic

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Jitendra Malik

Jitendra Malik

University of California, Berkeley

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Dinggang Shen

Dinggang Shen

ShanghaiTech University

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