第235页 7.5 Noise Robustness

Another way that has been used in the service of regularizing models is by adding it to the weights. This technique has been used primarily in the context of RNN (jim et al., 1996; Graves, 2011). This can be interpreted as a stochastic implementation of Bayesian inference over the weights. The Bayesian treatment of learning would consider the model weights to be uncertain and representable via a probability distribution that reflects this uncertainty. Adding noise to the weights is a practical, stochastic way to reflect this uncertainty. 
Noise applied to the weights can also be interpreted as equivalent (under some assumptions) to a more traditional form of regularization, encouraging stability of the function to be learned.