Learning representations by back-propagating errors
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Seminal 1986 Nature paper introducing backpropagation, a foundational algorithm enabling deep neural networks; relevant to AI safety as it established the learning mechanisms underlying modern deep learning systems whose alignment and interpretability are central safety concerns.
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This seminal 1986 Nature paper introduces backpropagation, a learning algorithm for multi-layer neural networks that iteratively adjusts connection weights to minimize the difference between actual and desired outputs. The key innovation is that hidden units in the network automatically learn to represent important features of the task domain through this error-driven learning process. This capability to discover useful internal representations distinguishes backpropagation from earlier methods like the perceptron, enabling neural networks to learn complex, non-linear relationships in data.
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| Page | Type | Quality |
|---|---|---|
| Geoffrey Hinton | Person | 42.0 |
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Learning representations by back-propagating errors | Nature
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Abstract
We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector. As a result of the weight adjustments, internal ‘hidden’ units which are not part of the input or output come to represent important features of the task domain, and the regularities in the task are captured by the interactions of these units. The ability to create useful new features distinguishes back-propagation from earlier, simpler methods such as the perceptron-convergence procedure 1 .
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References
Rosenblatt, F. Principles of Neurodynamics (Spartan, Washington, DC, 1961).
Google Scholar
Minsky, M. L. & Papert, S. Perceptrons (MIT, Cambridge, 1969).
MATH
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Rumelhart, D. E., Hinton, G. E. & Williams,
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