  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.
