VISUALIZING LEARNING AND COMPUTATION IN ARTIFICIAL NEURAL NETWORKS

Abstract

Scientific visualization is the process of using graphical images to form succinct and lucid representations of numerical data. Visualization has proven to be a useful method for understanding both learning and computation in artificial neural networks. While providing a powerful and general technique for inductive learning, artificial neural networks are difficult to comprehend because they form representations that are encoded by a large number of real-valued parameters. By viewing these parameters pictorially, a better understanding can be gained of how a network maps inputs into outputs. In this article, we survey a number of visualization techniques for understanding the learning and decision-making processes of neural networks. We also describe our work in knowledge-based neural networks and the visualization techniques we have used to understand these networks. In a knowledge-based neural network, the topology and initial weight values of the network are determined by an approximately-correct set of inference rules. Knowledge-based networks are easier to interpret than conventional networks because of the synergy between visualization methods and the relation of the networks to symbolic rules.