Loss regularization can help reduce the gap between training and test error by systematically limiting model complexity. Popular regularization techniques such as L2 weight regularization act directly on the network parameters, but do not explicitly take into account how the interplay between the parameters and the network architecture may affect the induced predictive functions.To address this shortcoming, we propose a simple technique for effective function-space regularization. Drawing on the…