Training dynamics of systems in a variable environment

The authors describe an approach to bounded training time and robustness in learning systems such as neural networks that is based on the topological properties of an associated flow. The method uses data transformations to create a modified problem for which the desired topological properties hold. A simple example, the two-layer perceptron, is used to illustrate the concepts considered here