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Using the Grow-And-Prune Network to Solve Problems of Large Dimensionality

This paper investigates a technique for creating sparsely connected feed-forward neural networks which may be capable of producing networks that have very large input and output layers. The architecture appears to be particularly suited to tasks that involve sparse training data as it is able to take advantage of the sparseness to further reduce training time. Some initial results are presented based on tests on the 16 bit compression problem.