A crossing activation function combined with virtual noise fields allows one neural network to learn multiple functions assigned to different noise locations, with capacity rising when noise arrangement matches function proximity.
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Spatial Partial Functionalization of Neural Networks based on Noise Fields
A crossing activation function combined with virtual noise fields allows one neural network to learn multiple functions assigned to different noise locations, with capacity rising when noise arrangement matches function proximity.