Letters: Prediction error of a fault tolerant neural network
Neurocomputing
The dropout learning algorithm
Artificial Intelligence
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We analyze the effects of analog noise on the synaptic arithmetic during multilayer perceptron training, by expanding the cost function to include noise-mediated terms. Predictions are made in the light of these calculations that suggest that fault tolerance, training quality and training trajectory should be improved by such noise-injection. Extensive simulation experiments on two distinct classification problems substantiate the claims. The results appear to be perfectly general for all training schemes where weights are adjusted incrementally, and have wide-ranging implications for all applications, particularly those involving “inaccurate” analog neural VLSI