Dropout: A Simple Way to Prevent Neural Networks from Overfitting

“Dropout: A Simple Way to Prevent Neural Networks from Overfitting” (2014) has been cited 62,059 times according to Google Scholar. CitationMap has resolved 10 citing papers from institutions across 7 countries.

Journal of Machine Learning Research2014View paper

Authors: Alex Krizhevsky (University of Toronto), Ilya Sutskever (University of Toronto), Ruslan Salakhutdinov (University of Toronto), Nitish Srivastava (University of Toronto), Geoffrey Hinton (University of Toronto)

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Where this paper is cited

China · 6United States · 4Australia · 2Pakistan · 1Saudi Arabia · 1Mexico · 1Hong Kong · 1

Top citing institutions

  • The University of Sydney (2)
  • Guangdong University of Finance and Economics (1)
  • Changchun University of Science and Technology (1)
  • Jinan University (1)
  • Mississippi State University (1)
  • University of Engineering and Technology (UET) (1)
  • The Chinese University of Hong Kong (1)
  • University of Technology Sydney and CSIRO (1)
  • King Fahd University of Petroleum and Minerals (1)
  • The University of Melbourne (1)
  • Australian National University (1)
  • The University of Western Australia (1)

Papers citing this work (10 resolved)

  1. · Xia Zhao, Limin Wang, Yufei Zhang, Xuming Han +2 more

  2. · Humza Naveed, Asad Ullah Khan, Shi Qiu, Muhammad Saqib +6 more

  3. · Jiacheng Ruan, Jincheng Li, Suncheng Xiang

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    Nature · 2024 · Trieu H. Trinh, Yuhuai Wu, Quoc V. Le, He He +1 more

  5. · Nikhila Ravi, Chloe Rolland, Laura Gustafson, Eric Mintun +8 more

  6. A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas

    · Diana-Margarita Córdova-Esparza, Julio-Alejandro Romero-González, Juan R. Terven

  7. The falcon series of open language models

    · Ebtesam Almazrouei, Julien Launay, Hamza Alobeidli, Quentin Malartic +10 more

  8. A comprehensive survey of continual learning: Theory, method and application

    · Liyuan Wang, Hang Su, Jun Zhu, Xingxing Zhang

  9. Heterogeneous federated learning: State-of-the-art and research challenges

    · Dacheng Tao, Bo Du, Mang Ye, Pong C. Yuen +1 more

  10. Language models are super mario: Absorbing abilities from homologous models as a free lunch

    · Fei Huang, Yu Bowen, Le Yu, Haiyang Yu +1 more

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