Speedup from a different parametrization within the Neural Network algorithm

Abstract

A different parametrization of the hyperplanes is used in the neural network algorithm. As demonstrated on several autoencoder examples it significantly outperforms the usual parametrization, reaching lower training error values with only a fraction of the number of epochs. It's argued that it makes it easier to understand and initialize the parameters.