Exact Dynamics in Feedforward Neural Networks
نویسنده
چکیده
I consider layered neural networks in which the weights are trained by optimizing an arbitrary performance function with respect to a set of examples. Using the cavity method and many-body diagrammatic techniques, the evolution in the network can be described by an overlap and a noise parameter. Parameter pairs corresponding to various input conditions are found to collapse on a universal curve. Simulations with the maximally stable network connrm the theory.
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