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Computer Science
Canada
2026

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Best Scientists

D-Index
223
Citations
683563
World Ranking
118
National Ranking
3

Computer Science

D-Index
224
Citations
651723
World Ranking
1
National Ranking
1

Yoshua Bengio publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Yoshua Bengio sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 856 publications — 99th percentile

99% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Yoshua Bengio D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Yoshua Bengio sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 224 D-Index — 100th percentile

100% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

  • 2026 - Research.com Computer Science in Canada Leader Award
  • 2025 - Research.com Best Scientists Award
  • 2025 - Research.com Computer Science in Canada Leader Award
  • 2023 - Research.com Computer Science in Canada Leader Award
  • 2022 - Research.com Computer Science in Canada Leader Award
  • 2020 - Fellow of the Royal Society, United Kingdom
  • 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.
  • 2019 - Neural Networks Pioneer Award, IEEE Computational Intelligence Society
  • 2019 - Izaak Walton Killam Memorial Prize, Canada Council
  • 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

Yoshua Bengio is affiliated with the University of Montreal in Canada. Their primary field of research is Computer Science with a specific focus on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Materials Chemistry, and Cognitive Neuroscience.

Their recent scientific contributions include the following papers:

  • "Generative adversarial networks", 2020, Communications of the ACM
  • "Static Analysis of Shape in TensorFlow Programs", 2020, arXiv (Cornell University)
  • "Scientific discovery in the age of artificial intelligence", 2023, Nature
  • "Machine learning for combinatorial optimization: A methodological tour d'horizon", 2021, Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna)
  • "Toward Causal Representation Learning", 2021, Proceedings of the IEEE

Frequent publication venues for their work include:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • SuperIntelligence - Robotics - Safety & Alignment
  • Science

Bengio's collaborative network features frequent co-authors such as:

  • Alexandre Lacoste
  • Evan David Sherwin
  • Pau Rodríguez
  • Alexandre Drouin
  • David Vázquez

The main topics of their research include:

  • Domain Adaptation and Few-Shot Learning
  • Machine Learning in Materials Science
  • Reinforcement Learning in Robotics
  • Neural Networks and Applications
  • Topic Modeling
  • Generative Adversarial Networks and Image Synthesis
  • Explainable Artificial Intelligence (XAI)

Throughout their career, Yoshua Bengio has received several awards and honors, including:

  • Fellow of the Royal Society, United Kingdom (2020)
  • Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) (2020), recognized for contributions to deep neural networks and scientific leadership
  • Neural Networks Pioneer Award, IEEE Computational Intelligence Society (2019)
  • Izaak Walton Killam Memorial Prize, Canada Council (2019)
  • A. M. Turing Award (2018), for conceptual and engineering breakthroughs in deep neural networks
  • Prix Marie-Victorin, Government of Quebec (2017)
  • Fellow of the Royal Society of Canada (2017), Academy of Science

Best Publications

  • Deep learning

    Yann LeCun;Yann LeCun;Yoshua Bengio;Geoffrey Hinton;Geoffrey Hinton

  • Gradient-based learning applied to document recognition

    Yann Lecun;Leon Bottou;Leon Bottou;Yoshua Bengio;Yoshua Bengio;Yoshua Bengio;Patrick Haffner;Patrick Haffner

  • Generative Adversarial Nets

    Ian Goodfellow;Jean Pouget-Abadie;Mehdi Mirza;Bing Xu

  • Deep Learning

    Ian Goodfellow;Yoshua Bengio;Aaron Courville

  • Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation

    Kyunghyun Cho;Bart van Merrienboer;Caglar Gulcehre;Dzmitry Bahdanau

  • Neural Machine Translation by Jointly Learning to Align and Translate

    Dzmitry Bahdanau;Kyunghyun Cho;Yoshua Bengio

  • Understanding the difficulty of training deep feedforward neural networks

    Xavier Glorot;Yoshua Bengio

  • Generative adversarial networks

    Ian Goodfellow;Jean Pouget-Abadie;Mehdi Mirza;Bing Xu

  • Representation Learning: A Review and New Perspectives

    Y. Bengio;A. Courville;P. Vincent

  • Empirical evaluation of gated recurrent neural networks on sequence modeling

    Junyoung Chung;Çaglar Gülçehre;KyungHyun Cho;Yoshua Bengio;Yoshua Bengio;Yoshua Bengio

  • Learning long-term dependencies with gradient descent is difficult

    Y. Bengio;P. Simard;P. Frasconi

  • Learning Deep Architectures for AI

    Yoshua Bengio

  • Random search for hyper-parameter optimization

    James Bergstra;Yoshua Bengio

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

    Kelvin Xu;Jimmy Ba;Ryan Kiros;Kyunghyun Cho

  • Deep sparse rectifier neural networks

    Xavier Glorot;Antoine Bordes;Yoshua Bengio

  • Extracting and composing robust features with denoising autoencoders

    Pascal Vincent;Hugo Larochelle;Yoshua Bengio;Pierre-Antoine Manzagol

  • A neural probabilistic language model

    Yoshua Bengio;Réjean Ducharme;Pascal Vincent;Christian Janvin

  • On the Properties of Neural Machine Translation: Encoder--Decoder Approaches

    Kyunghyun Cho;Bart van Merrienboer;Dzmitry Bahdanau;Yoshua Bengio;Yoshua Bengio;Yoshua Bengio

  • Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

    Pascal Vincent;Hugo Larochelle;Isabelle Lajoie;Yoshua Bengio

  • Convolutional networks for images, speech, and time series

    Yann LeCun;Yoshua Bengio;Yoshua Bengio;Yoshua Bengio

  • How transferable are features in deep neural networks

    Jason Yosinski;Jeff Clune;Yoshua Bengio;Hod Lipson

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

    Kelvin Xu;Jimmy Ba;Ryan Kiros;Kyunghyun Cho

  • A Neural Probabilistic Language Model

    Yoshua Bengio;Réjean Ducharme;Pascal Vincent

Frequent Co-Authors

Aaron Courville
Aaron Courville University of Montreal
Kyunghyun Cho
Kyunghyun Cho New York University
Pascal Vincent
Pascal Vincent Facebook (United States)
Caglar Gulcehre
Caglar Gulcehre DeepMind (United Kingdom)
Razvan Pascanu
Razvan Pascanu DeepMind (United Kingdom)
Chris Pal
Chris Pal Polytechnique Montréal
Hugo Larochelle
Hugo Larochelle Google (United States)
Ian Goodfellow
Ian Goodfellow Google (United States)
Adam Trischler
Adam Trischler Microsoft (United States)
Joelle Pineau
Joelle Pineau McGill University

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