Before revision:  We apply the model to the subset of numbers "0" and "1" of the classical MNIST database, where we are able to discern individuals from both classes with more than 94\\%DIF < accuracy, sometimes using less than 80 variables, a feature that is out of reach of Artificial Neural Networks without weight sharing or feature engineering.
After revision:  We apply the model to the subset of numbers "0" and "1" of the classical MNIST database, where we are able to discern individuals with more than 94\\%DIF < accuracy, sometimes using less than 80 variables, a feature that is out of reach of Artificial Neural Networks without weight sharing or feature engineering.