“Learning representations by back-propagating errors”
E11117
“Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
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| Learning representations by back-propagating errors | 1 |
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| Predicate | Object |
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| instanceOf |
landmark paper in machine learning
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research article ⓘ scientific paper ⓘ |
| algorithmType | gradient-based learning algorithm ⓘ |
| author |
David E. Rumelhart
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Geoffrey Hinton ⓘ
surface form:
Geoffrey E. Hinton
Ronald J. Williams ⓘ |
| citationStatus | highly cited paper in machine learning ⓘ |
| contribution |
demonstrated that internal representations can be learned by gradient descent
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popularized backpropagation for training multi-layer neural networks ⓘ showed that distributed representations can solve complex pattern recognition tasks ⓘ |
| era | connectionist revival of the 1980s ⓘ |
| field |
artificial intelligence
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deep learning ⓘ machine learning ⓘ neural networks ⓘ |
| focus |
learning internal hidden-unit representations
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training feedforward neural networks ⓘ |
| historicalSignificance | helped launch the modern field of deep learning ⓘ |
| influencedField |
computer vision
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deep learning ⓘ natural language processing ⓘ speech recognition ⓘ |
| language | English ⓘ |
| learningParadigm | error backpropagation ⓘ |
| mainTopic |
backpropagation algorithm
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multi-layer neural networks ⓘ representation learning ⓘ supervised learning ⓘ |
| method |
chain rule of calculus for error propagation
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gradient descent on error function ⓘ |
| networkType | multi-layer perceptron ⓘ |
| publicationYear | 1986 ⓘ |
| publishedIn | Nature ⓘ |
| publisher | Nature Publishing Group ⓘ |
| relatedAlgorithm | backpropagation ⓘ |
| relatedConcept |
credit assignment problem
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distributed representations ⓘ gradient-based optimization ⓘ |
| title |
“Learning representations by back-propagating errors”
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Learning representations by back-propagating errors
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Learning representations by back-propagating errors