Yoshua Bengio

1.3k total papers · 441.4k total citations
414 papers, 191.0k citations indexed

About

Yoshua Bengio is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Yoshua Bengio has authored 414 papers receiving a total of 191.0k indexed citations (citations by other indexed papers that have themselves been cited), including 303 papers in Artificial Intelligence, 160 papers in Computer Vision and Pattern Recognition and 58 papers in Signal Processing. Recurrent topics in Yoshua Bengio's work include Neural Networks and Applications (105 papers), Generative Adversarial Networks and Image Synthesis (62 papers) and Topic Modeling (59 papers). Yoshua Bengio is often cited by papers focused on Neural Networks and Applications (105 papers), Generative Adversarial Networks and Image Synthesis (62 papers) and Topic Modeling (59 papers). Yoshua Bengio collaborates with scholars based in Canada, United States and United Kingdom. Yoshua Bengio's co-authors include Yann LeCun, Geoffrey E. Hinton, Léon Bottou, Patrick Haffner, Aaron Courville, Xavier Glorot, Ian Goodfellow, James Bergstra, Pascal Vincent and Hugo Larochelle and has published in prestigious journals such as Nature, Science and The Journal of Chemical Physics.

In The Last Decade

Yoshua Bengio

399 papers receiving 182.8k citations

Hit Papers

Deep learning 1994 2026 2004 2015 2015 1998 2020 2010 2013 10.0k 20.0k 30.0k 40.0k 50.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation benchmark for papers published in the same subfield and year, or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep learning
    2015 53.0k citations Yann LeCun, Yoshua Bengio et al. Nature profile →
  2. Gradient-based learning applied to document recognition
    1998 34.1k citations Yann LeCun, Léon Bottou et al. Proceedings of the IEEE profile →
  3. Generative adversarial networks
    2020 7.9k citations Ian Goodfellow, Jean Pouget-Abadie et al. Communications of the ACM profile →
  4. Understanding the difficulty of training deep feedforward neural networks
    2010 7.8k citations Xavier Glorot, Yoshua Bengio Journal of Machine Learning Research profile →
  5. Representation Learning: A Review and New Perspectives
    2013 7.7k citations Yoshua Bengio, Aaron Courville et al. IEEE Transactions on Pattern Analysis and Machine Intelligence profile →
  6. Deep Learning
    2016 5.8k citations Ian Goodfellow, Yoshua Bengio et al. MIT Press eBooks profile →
  7. Random search for hyper-parameter optimization
    2012 5.5k citations James Bergstra, Yoshua Bengio profile →
  8. Learning long-term dependencies with gradient descent is difficult
    1994 5.4k citations Yoshua Bengio, P. Simard et al. IEEE Transactions on Neural Networks profile →
  9. Learning Deep Architectures for AI
    2009 4.8k citations Yoshua Bengio profile →
  10. Extracting and composing robust features with denoising autoencoders
    2008 4.4k citations Pascal Vincent, Hugo Larochelle et al. profile →
  11. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
    2015 4.2k citations Kelvin Xu, Jimmy Ba et al. arXiv (Cornell University) profile →
  12. Proceedings of the 21st International Conference on Neural Information Processing Systems
    2008 3.5k citations Daphne Koller, Dale Schuurmans et al. profile →
  13. Learning Deep Architectures for AI
    2009 3.4k citations Yoshua Bengio now publishers, Inc. eBooks profile →
  14. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
    2010 3.3k citations Pascal Vincent, Hugo Larochelle et al. Journal of Machine Learning Research profile →
  15. Curriculum learning
    2009 2.5k citations Yoshua Bengio, Jérôme Louradour et al. profile →
  16. How transferable are features in deep neural networks?
    2014 2.3k citations Jason Yosinski, Jeff Clune et al. arXiv (Cornell University) profile →
  17. Brain tumor segmentation with Deep Neural Networks
    2016 2.1k citations Mohammad Havaei, Axel Davy et al. Medical Image Analysis profile →
  18. Why Does Unsupervised Pre-training Help Deep Learning?
    2010 1.4k citations Dumitru Erhan, Yoshua Bengio et al. Journal of Machine Learning Research profile →
  19. Word Representations: A Simple and General Method for Semi-Supervised Learning
    2010 1.2k citations Joseph Turian, Yoshua Bengio et al. Meeting of the Association for Computational Linguistics profile →
  20. The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
    2017 1.1k citations Simon Jégou, Michal Drozdzal et al. PolyPublie (École Polytechnique de Montréal) profile →
  21. Exploring Strategies for Training Deep Neural Networks
    2009 757 citations Hugo Larochelle, Yoshua Bengio et al. Journal of Machine Learning Research profile →
  22. HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
    2018 717 citations Zhilin Yang, Peng Qi et al. profile →
  23. Machine learning for combinatorial optimization: A methodological tour d'horizon
    2021 694 citations Yoshua Bengio, Andrea Lodi et al. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) profile →
  24. Theano: A CPU and GPU Math Compiler in Python
    2010 685 citations James Bergstra, Olivier Breuleux et al. Proceedings of the Python in Science Conferences profile →
  25. Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
    2011 674 citations Salah Rifai, Pascal Vincent et al. International Conference on Machine Learning profile →
  26. A Neural Probabilistic Language Model
    2000 674 citations Yoshua Bengio, Réjean Ducharme et al. profile →
  27. An empirical evaluation of deep architectures on problems with many factors of variation
    2007 665 citations Hugo Larochelle, Dumitru Erhan et al. profile →
  28. Scaling learning algorithms towards AI
    2007 615 citations Yoshua Bengio, Yann LeCun profile →
  29. Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering
    2003 599 citations Yoshua Bengio, Pascal Vincent et al. profile →
  30. Semi-supervised Learning by Entropy Minimization
    2004 527 citations Yves Grandvalet, Yoshua Bengio Neural Information Processing Systems profile →
  31. Representational Power of Restricted Boltzmann Machines and Deep Belief Networks
    2008 527 citations Nicolas Le Roux, Yoshua Bengio Neural Computation profile →
  32. Deep Learning of Representations for Unsupervised and Transfer Learning.
    2011 517 citations Yoshua Bengio International Conference on Machine Learning profile →
  33. No Unbiased Estimator of the Variance of K-Fold Cross-Validation
    2003 513 citations Yoshua Bengio, Yves Grandvalet Journal of Machine Learning Research profile →
  34. Toward Causal Representation Learning
    2021 505 citations Bernhard Schölkopf, Francesco Locatello et al. Proceedings of the IEEE profile →
  35. On Using Very Large Target Vocabulary for Neural Machine Translation
    2015 464 citations Sébastien Jean, Kyunghyun Cho et al. profile →
  36. Hierarchical Probabilistic Neural Network Language Model.
    2005 450 citations Yoshua Bengio et al. International Conference on Artificial Intelligence and Statistics profile →
  37. Classification using discriminative restricted Boltzmann machines
    2008 449 citations Hugo Larochelle, Yoshua Bengio profile →
  38. A semantic matching energy function for learning with multi-relational data
    2013 413 citations Antoine Bordes, Xavier Glorot et al. Machine Learning profile →
  39. How to Construct Deep Recurrent Neural Networks
    2014 399 citations Razvan Pascanu, Çaǧlar Gülçehre et al. International Conference on Learning Representations profile →
  40. Maxout Networks
    2013 381 citations Ian Goodfellow, David Warde-Farley et al. profile →
  41. Deep learning for AI
    2021 346 citations Yoshua Bengio, Yann LeCun et al. Communications of the ACM profile →
  42. A closer look at memorization in deep networks
    2017 343 citations Devansh Arpit, Stanisław Jastrzȩbski et al. Jagiellonian University Repository (Jagiellonian University) profile →
  43. An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
    2014 340 citations Ian Goodfellow, Mehdi Mirza et al. arXiv (Cornell University) profile →
  44. BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
    2016 319 citations Matthieu Courbariaux, Yoshua Bengio arXiv (Cornell University) profile →
  45. Mutual Information Neural Estimation.
    2018 300 citations Mohamed Ishmael Belghazi, Aristide Baratin et al. International Conference on Machine Learning profile →
  46. A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion
    2015 269 citations Alessandro Sordoni, Yoshua Bengio et al. Research at the University of Copenhagen (University of Copenhagen) profile →
  47. Light Gated Recurrent Units for Speech Recognition
    2018 267 citations Mirco Ravanelli, Philémon Brakel et al. IEEE Transactions on Emerging Topics in Computational Intelligence profile →
  48. Inductive biases for deep learning of higher-level cognition
    2022 126 citations Anirudh Goyal, Yoshua Bengio Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Yoshua Bengio 80.1k 61.0k 18.5k 15.1k 13.2k 414 191.0k
Geoffrey E. Hinton 101.6k 1.3× 80.6k 1.3× 23.1k 1.3× 25.9k 1.7× 16.3k 1.2× 256 256.9k
Yann LeCun 48.1k 0.6× 47.7k 0.8× 14.3k 0.8× 9.0k 0.6× 9.5k 0.7× 193 131.7k
Xiangyu Zhang 51.2k 0.6× 86.4k 1.4× 10.2k 0.6× 9.7k 0.6× 15.5k 1.2× 200 159.9k
Vladimir Vapnik 56.8k 0.7× 37.4k 0.6× 11.4k 0.6× 12.4k 0.8× 4.9k 0.4× 89 153.6k
Jian Sun 45.5k 0.6× 75.5k 1.2× 8.6k 0.5× 8.0k 0.5× 13.8k 1.1× 73 137.9k
Jürgen Schmidhuber 43.2k 0.5× 23.4k 0.4× 14.1k 0.8× 11.3k 0.7× 3.4k 0.3× 241 103.8k
Kaiming He 73.2k 0.9× 150.1k 2.5× 14.0k 0.8× 11.5k 0.8× 21.6k 1.6× 54 250.1k
Andrew Zisserman 34.1k 0.4× 100.4k 1.6× 6.1k 0.3× 9.8k 0.7× 7.6k 0.6× 447 137.5k
Robert Tibshirani 37.3k 0.5× 17.5k 0.3× 7.0k 0.4× 8.1k 0.5× 8.5k 0.6× 402 237.3k
Li Fei-Fei 43.0k 0.5× 69.0k 1.1× 4.1k 0.2× 5.3k 0.4× 7.3k 0.6× 370 105.8k

Countries citing papers authored by Yoshua Bengio

Since Specialization
Citations

This map shows the geographic impact of Yoshua Bengio's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yoshua Bengio with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yoshua Bengio more than expected).

Fields of papers citing papers by Yoshua Bengio

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yoshua Bengio. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yoshua Bengio. The network helps show where Yoshua Bengio may publish in the future.

Co-authorship network of co-authors of Yoshua Bengio

This figure shows the co-authorship network connecting the top 25 collaborators of Yoshua Bengio. A scholar is included among the top collaborators of Yoshua Bengio based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yoshua Bengio. Yoshua Bengio is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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