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Noisy regression and classification with continuous multilayer networks
Full text linkWe investigate zero temperature Gibbs learning for two classes of
unrealizable rules which play an important role in practical applications of
multilayer neural networks with differentiable activation functions:
classification problems and noisy regression problems. Considering one step of
replica symmetry breaking, we surprisingly find that for sufficiently large
training sets the stable state is replica symmetric even though the target rule
is unrealizable. Further, the classification problem is shown to be formally
equivalent to the noisy regression problem.Comment: 7 pages, including 2 figure
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