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- W2079741111 abstract "The influence of biases on the learning dynamics of a two-layer neural network, a normalized soft-committee machine, is studied for on-line gradient descent learning. Within a statistical mechanics framework, numerical studies show that the inclusion of adjustable biases dramatically alters the learning dynamics found previously. The symmetric phase that has often been predominant in the original model all but disappears for a nondegenerate bias task. The extended model furthermore exhibits a much richer dynamical behavior, e.g., attractive suboptimal symmetric phases even for realizable cases and noiseless data." @default.
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- W2079741111 title "Role of biases in on-line learning of two-layer networks" @default.
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