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improving-neural-networks-by-preventing-co-adaptation-of-feature-detectors-2020-007

Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan R. SalakhutdinovUFTAN UNIVERSITYComputer Science2020

Abstract

This record links to the dropout paper, which explains how randomly omitting feature detectors during training can reduce overfitting. It is suitable for testing undergraduate AI project metadata, concise abstracts, and direct external PDF linking.

Metadata

Author
Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan R. Salakhutdinov
Supervisor
Dr. Blessing O. Danjuma
Faculty
Science
Department
Computer Science
Document type
Final Year Project
Year
2020
Keywords
dropout, neural networks, regularization, feature detectors, model generalization
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all_rights_reserved

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Source fields used: Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan R. Salakhutdinov
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