%Aigaion2 BibTeX export from Idiap Publications
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@ARTICLE{Moerland-98.1,
                      author = {Moerland, Perry and Fiesler, Emile and Saxena, Indu},
                    projects = {Idiap},
                       month = {4},
                       title = {Discrete All-Positive Multilayer Perceptrons for Optical Implementation},
                     journal = {Optical Engineering},
                      volume = {37},
                      number = {4},
                        year = {1998},
                        note = {(IDIAP-RR 97-02)},
                    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},
ipdinar={1997},
ipdmembership={learning},
}