Can deterministic penalty terms model the effects of synaptic weight noise on network fault-tolerance?

This paper investigates fault tolerance in feedforward neural networks, for a realistic fault model based on analog hardware. In our previous work with synaptic weight noise we showed significant fault tolerance enhancement over standard training algorithms. We proposed that when introduced into training, weight noise distributes the network computation more evenly across the weights and thus enhances fault tolerance. Here we compare those results with an approximation to the mechanisms induced by stochastic weight noise, incorporated into training deterministically via penalty terms. The penalty terms are an approximation to weight saliency and therefore, in addition, we assess a number of other weight saliency measures and perform comparison experiments. The results show that the first term approximation is an incomplete model of weight noise in terms of fault tolerance. Also the error Hessian is shown to be the most accurate measure of weight saliency.