%Aigaion2 BibTeX export from Idiap Publications
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@TECHREPORT{Moerland-97.2,
                      author = {Moerland, Perry and Fiesler, Emile and Saxena, Indu},
                    projects = {Idiap},
                       title = {Discrete All-Positive Multilayer Perceptrons for Optical Implementation},
                        type = {Idiap-RR},
                      number = {Idiap-RR-02-1997},
                        year = {1997},
                 institution = {IDIAP},
                        note = {Accepted for publication in {\em Optical Engineering}},
                    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 limited accuracy of the weights. In this paper, a backpropagation-based learning rule is presented that compensates for these non-idealities and enables the implementation of all-optical multilayer perceptrons where learning occurs under control of a computer. The good performance of this learning rule, even when using a small number of weight levels, is illustrated by a series of experiments including the non-idealities.},
                         pdf = {https://publications.idiap.ch/attachments/reports/1997/rr97-02.pdf},
                  postscript = {ftp://ftp.idiap.ch/pub/reports/1997/rr97-02.ps.gz},
ipdmembership={learning},
}