Integration of collective predictions and symmetry breaking in a modular neural network
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چکیده
We study the generalization capability of a modular neural network`the mixture of experts' that learn from examples generated by another network with the same architecture. When the number of examples is smaller than a critical value, the network shows a symmetric phase where the role of the experts is not specialized. Upon crossing the critical point, the system undergoes a continuous phase transition to a symmetry-breaking phase where the gating network partitions the input space eeectively so that each expert is assigned to an appropriate subspace. We also nd that the mixture of experts with multiple levels of hierarchy shows multiple phase transitions.
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تاریخ انتشار 2007