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30 May 2020
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On model selection and the disability of neural networks to decompose tasks
Marc Toussaint ;
Date 19 Feb 2002
Journal Proceedings of the International Joint Conference on Neural Networks (IJCNN 2002), 245-250.
Subject Adaptation and Self-Organizing Systems; Neural and Evolutionary Computing; Disordered Systems and Neural Networks | nlin.AO cond-mat.dis-nn cs.NE
AbstractA neural network with fixed topology can be regarded as a parametrization of functions, which decides on the correlations between functional variations when parameters are adapted. We propose an analysis, based on a differential geometry point of view, that allows to calculate these correlations. In practise, this describes how one response is unlearned while another is trained. Concerning conventional feed-forward neural networks we find that they generically introduce strong correlations, are predisposed to forgetting, and inappropriate for task decomposition. Perspectives to solve these problems are discussed.
Source arXiv, nlin.AO/0202038
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