Wood, J. and Shawe-Taylor, J.
Representation Theory and Invariant Neural Networks.
Discrete Applied Mathematics, 69, (1-2), .
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A feedforward neural network is a computational device used for pattern recognition. In many recognition problems, certain transformations exist which, when applied to a pattern, leave its classification unchanged. Invariance under a given group of transformations is therefore typically a desirable property of pattern classifiers. In this paper, we present a methodology, based on representation theory, for the construction of a neural network invariant under any given finite linear group. Such networks show improved generalization abilities and may also learn faster than corresponding networks without in-built invariance. We hope in the future to generalize this theory to approximate invariance under continuous groups.
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