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- W2747062012 abstract "Abstract : The authors present training and feedforwardcomputation for a single layer of a VMM WTA classifier.The experimental demonstration of the one-layer universalapproximator encourages the use of one-layer networks forembedded low-power classification. The results enablingcorrect classification of each novel acoustic signal(generator, idle car, and idle truck). The classificationstructure requires, after training, less than 30W ofoperational power and lower with additional fabrication." @default.
- W2747062012 created "2017-08-31" @default.
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- W2747062012 date "2016-03-31" @default.
- W2747062012 modified "2023-09-26" @default.
- W2747062012 title "Learning for VMM + WTA Embedded Classifiers" @default.
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