H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 197 Citations 637,950 715 World Ranking 2 National Ranking 1

Research.com Recognitions

Awards & Achievements

2020 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For foundational contributions to development of deep neural networks, scientific leadership in Canada, and service to the AI community.

2020 - Fellow of the Royal Society, United Kingdom

2019 - Izaak Walton Killam Memorial Prize, Canada Council

2019 - Neural Networks Pioneer Award, IEEE Computational Intelligence Society

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

2017 - Fellow of the Royal Society of Canada Academy of Science

2017 - Prix Marie-Victorin, Government of Quebec

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary scientific interests are in Artificial intelligence, Machine learning, Artificial neural network, Deep learning and Recurrent neural network. The concepts of his Artificial intelligence study are interwoven with issues in Natural language processing and Pattern recognition. His Machine learning research focuses on subjects like Benchmark, which are linked to Visualization, Object detection, Computer vision and Backpropagation.

His Artificial neural network study combines topics in areas such as Encoder, Representation, Feature and Machine translation. The concepts of his Recurrent neural network study are interwoven with issues in High dimensional and Hidden Markov model. The study incorporates disciplines such as Optical character recognition, Vanishing gradient problem, Convolutional Deep Belief Networks, Intelligent character recognition and Neocognitron in addition to Handwriting recognition.

His most cited work include:

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

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

Yoshua Bengio mainly investigates Artificial intelligence, Artificial neural network, Machine learning, Algorithm and Deep learning. He combines subjects such as Natural language processing, Speech recognition and Pattern recognition with his study of Artificial intelligence. As part of his studies on Speech recognition, Yoshua Bengio often connects relevant subjects like Encoder.

His Artificial neural network research incorporates elements of Generalization, Computation and Convolutional neural network. His Machine learning study incorporates themes from Inference and Benchmark. The Algorithm study combines topics in areas such as Function, Sampling and Estimator.

He most often published in these fields:

  • Artificial intelligence (76.15%)
  • Artificial neural network (37.23%)
  • Machine learning (29.97%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (76.15%)
  • Machine learning (29.97%)
  • Artificial neural network (37.23%)

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

Artificial intelligence, Machine learning, Artificial neural network, Reinforcement learning and Deep learning are his primary areas of study. His biological study spans a wide range of topics, including Generalization and Pattern recognition. His studies in Machine learning integrate themes in fields like Adversarial system, Structure, Sample and Adaptation.

His research integrates issues of Algorithm, Computation and Robustness in his study of Artificial neural network. His Reinforcement learning research integrates issues from Context, State, Human–computer interaction and Set. His work carried out in the field of Deep learning brings together such families of science as Representation and Residual.

Between 2018 and 2021, his most popular works were:

  • A deep learning framework for neuroscience (199 citations)
  • A deep learning framework for neuroscience (199 citations)
  • Manifold Mixup: Better Representations by Interpolating Hidden States (193 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Deep learning, Artificial neural network and Reinforcement learning. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Encoder. Yoshua Bengio usually deals with Machine learning and limits it to topics linked to Interpolation and Decision boundary and Consistency.

Yoshua Bengio interconnects Pneumonia, Speech recognition, Cognition, Treatment efficacy and Residual in the investigation of issues within Deep learning. His work deals with themes such as Algorithm and Computation, which intersect with Artificial neural network. In his study, which falls under the umbrella issue of Reinforcement learning, Measure is strongly linked to Set.

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

Generative Adversarial Nets

Ian Goodfellow;Jean Pouget-Abadie;Mehdi Mirza;Bing Xu.
neural information processing systems (2014)

22768 Citations

Deep Learning

Ian Goodfellow;Yoshua Bengio;Aaron Courville.
(2016)

20521 Citations

Understanding the difficulty of training deep feedforward neural networks

Xavier Glorot;Yoshua Bengio.
international conference on artificial intelligence and statistics (2010)

10076 Citations

Neural Machine Translation by Jointly Learning to Align and Translate

Dzmitry Bahdanau;Kyunghyun Cho;Yoshua Bengio.
international conference on learning representations (2015)

10031 Citations

Learning Deep Architectures for AI

Yoshua Bengio.
(2009)

8773 Citations

Representation Learning: A Review and New Perspectives

Y. Bengio;A. Courville;P. Vincent.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2013)

7322 Citations

A neural probabilistic language model

Yoshua Bengio;Réjean Ducharme;Pascal Vincent;Christian Janvin.
Journal of Machine Learning Research (2003)

6871 Citations

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Kelvin Xu;Jimmy Ba;Ryan Kiros;Kyunghyun Cho.
international conference on machine learning (2015)

5793 Citations

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Best Scientists Citing Yoshua Bengio

Dacheng Tao

Dacheng Tao

University of Sydney

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Björn Schuller

Björn Schuller

Imperial College London

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Kyunghyun Cho

New York University

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Yann LeCun

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Facebook (United States)

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Luc Van Gool

Luc Van Gool

ETH Zurich

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Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

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Jianfeng Gao

Jianfeng Gao

Microsoft (United States)

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Shuicheng Yan

Shuicheng Yan

National University of Singapore

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Ruslan Salakhutdinov

Ruslan Salakhutdinov

Carnegie Mellon University

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Xiaogang Wang

Xiaogang Wang

Chinese University of Hong Kong

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Dinggang Shen

Dinggang Shen

ShanghaiTech University

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Lawrence Carin

Lawrence Carin

King Abdullah University of Science and Technology

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Trevor Darrell

Trevor Darrell

University of California, Berkeley

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Jiebo Luo

Jiebo Luo

University of Rochester

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Yi Yang

Yi Yang

Zhejiang University

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Chunhua Shen

Chunhua Shen

University of Adelaide

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