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Deep learning

Author

Listed:
  • Yann LeCun

    (Facebook AI Research
    New York University)

  • Yoshua Bengio

    (Pavillon André-Aisenstadt)

  • Geoffrey Hinton

    (Google
    University of Toronto)

Abstract

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Suggested Citation

  • Yann LeCun & Yoshua Bengio & Geoffrey Hinton, 2015. "Deep learning," Nature, Nature, vol. 521(7553), pages 436-444, May.
    • Handle: RePEc:nat:nature:v:521:y:2015:i:7553:d:10.1038_nature14539
    DOI: 10.1038/nature14539
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    Citations

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    Cited by:

    1. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
    2. Tan, Zhixue & Zhong, Shisheng & Lin, Lin, 2019. "Trans-layer model learning: A hierarchical modeling strategy for real-time reliability evaluation of complex systems," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 120-132.
    3. Shigeyuki Hamori & Takahiro Kume, 2018. "Artificial Intelligence And Economic Growth," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 256-278, December.
    4. Hannes Mueller & Andre Groger & Jonathan Hersh & Andrea Matranga & Joan Serrat, 2020. "Monitoring War Destruction from Space: A Machine Learning Approach," Papers 2010.05970, arXiv.org, revised Oct 2020.
    5. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
    6. Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    7. Nikolaos Passalis & Anastasios Tefas & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data," Papers 1901.08280, arXiv.org.
    8. Changran He & Guoye Wang & Zhangpeng Gong & Zhichao Xing & Dongxin Xu, 2018. "A Control Algorithm for the Novel Regenerative–Mechanical Coupled Brake System with by-Wire Based on Multidisciplinary Design Optimization for an Electric Vehicle," Energies, MDPI, vol. 11(9), pages 1-18, September.
    9. Hoon Lee & Han Seung Jang & Bang Chul Jung, 2019. "Improving Energy Efficiency Fairness of Wireless Networks: A Deep Learning Approach," Energies, MDPI, vol. 12(22), pages 1-18, November.
    10. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2020. "Deep Learning for Portfolio Optimization," Papers 2005.13665, arXiv.org, revised Jan 2021.
    11. Nanne, Annemarie J. & Antheunis, Marjolijn L. & van der Lee, Chris G. & Postma, Eric O. & Wubben, Sander & van Noort, Guda, 2020. "The Use of Computer Vision to Analyze Brand-Related User Generated Image Content," Journal of Interactive Marketing, Elsevier, vol. 50(C), pages 156-167.
    12. Shuaiqiang Liu & Lech A. Grzelak & Cornelis W. Oosterlee, 2022. "The Seven-League Scheme: Deep Learning for Large Time Step Monte Carlo Simulations of Stochastic Differential Equations," Risks, MDPI, vol. 10(3), pages 1-27, February.
    13. Jian-Huang She & Dan Grecu, 2018. "Neural Network for CVA: Learning Future Values," Papers 1811.08726, arXiv.org.
    14. Wang, Shenhao & Wang, Qingyi & Zhao, Jinhua, 2020. "Multitask learning deep neural networks to combine revealed and stated preference data," Journal of choice modelling, Elsevier, vol. 37(C).
    15. Baptiste Barreau & Laurent Carlier, 2020. "History-Augmented Collaborative Filtering for Financial Recommendations," Post-Print hal-03144669, HAL.
    16. Baptiste Barreau & Laurent Carlier, 2021. "History-Augmented Collaborative Filtering for Financial Recommendations," Papers 2102.13503, arXiv.org.
    17. Huang, Qian & Li, Jinghua & Zhu, Mengshu, 2020. "An improved convolutional neural network with load range discretization for probabilistic load forecasting," Energy, Elsevier, vol. 203(C).
    18. Hannes Mueller & André Groeger & Jonathan Hersh & Andrea Matranga & Joan Serrat, 2021. "Monitoring War Destruction from Space Using Machine Learning," Working Papers 1257, Barcelona School of Economics.
    19. Ariel Navon & Yosi Keller, 2017. "Financial Time Series Prediction Using Deep Learning," Papers 1711.04174, arXiv.org.
    20. Qiang Zhang & Rui Luo & Yaodong Yang & Yuanyuan Liu, 2018. "Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series," Papers 1811.03711, arXiv.org.
    21. Anping Song & Zuoyu Wu & Xuehai Ding & Qian Hu & Xinyi Di, 2018. "Neurologist Standard Classification of Facial Nerve Paralysis with Deep Neural Networks," Future Internet, MDPI, vol. 10(11), pages 1-13, November.
    22. Crane-Droesch, Andrew, 2017. "Semiparametric Panel Data Using Neural Networks," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258128, Agricultural and Applied Economics Association.

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