Backpropagation
Backpropagation is a supervised learning algorithm for training artificial neural networks that computes the gradient of the loss function with respect to the weights of the network by applying the chain rule of calculus in a backward pass through the network.
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2017–2026
2K
Variants
Backward PropagationBackward Propagation Of Errors
Parents
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Sepp Hochreiter, Jürgen Schmidhuber · Neural Computation · 1997 · 98.1K citations
Learning representations by back-propagating errors
David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams · Nature · 1986 · 30.9K citations
Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell B. Stinchcombe, Halbert White · Neural Networks · 1989 · 21.2K citations
Backpropagation Applied to Handwritten Zip Code Recognition
Yann LeCun, Bernhard E. Boser, J. S. Denker et al. · Neural Computation · 1989 · 11.9K citations
Multilayer feedforward networks are universal approximators
HornikK., StinchcombeM., WhiteH. · Neural Networks · 1989 · 9.4K citations
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University of California San Diego
San Diego, United States
30 h-index
57 publications
120.4K citations
Tsinghua University
Beijing, China
29 h-index
159 publications
19.5K citations
Carnegie Mellon University
Pittsburgh, United States
28 h-index
62 publications
46.1K citations
Stanford University
Stanford, United States
28 h-index
76 publications
18K citations
Nanyang Technological University
Singapore, Singapore
26 h-index
94 publications
8.2K citations

Chinese Academy of Sciences
Beijing, China
26 h-index
161 publications
8.3K citations
National Yunlin University of Science and Technology
Douliu, Taiwan
26 h-index
60 publications
2.3K citations
National University of Singapore
Singapore, Singapore
25 h-index
96 publications
7.2K citations
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98 publications · 46.6K citations
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Repository · 205 publications · 29.7K citations
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87 publications · 137.7K citations
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87 publications · 7.9K citations
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