The article discusses how to decide when to stop training a neural network to prevent overfitting. By using a systematic approach, researchers found that stopping training earlier can lead to faster learning but may reduce the network's ability to generalize well. They tested this on different problems and network designs, showing that a slower stopping criterion can slightly improve generalization but at the cost of longer training time.