Publication

Overcoming Inaccuracies in Optical Multilayer Perceptrons

1996
Conference Papers

Abstract

All-optical multilayer perceptrons differ in various ways from the ideal neural network model. Examples are the use of non-ideal activation functions which are truncated, asymmetric, and have a non-standard gain, restriction of the network parameters to non-negative values, and the use of limited accuracy for the weights. In this paper an adaptation of the backpropagation learning rule is presented that compensates for these three non-idealities. The good performance of this learning rule is illustrated by a series of experiments. This algorithm enables the implementation of all-optical multilayer perceptrons where learning occurs under control of a computer.

Official source

https://infoscience.epfl.ch/entities/publication/a6f32cf3-9c3d-4655-ae9b-e0c8686cc2a5

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1996
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/a6f32cf3-9c3d-4655-ae9b-e0c8686cc2a5

About this result

Related concepts (32)

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Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

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Related publications (9)

Overcoming Inaccuracies in Optical Multilayer Perceptrons

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