On the Stability and Generalization of Learning with Kernel Activation Functions

28 Mar 2019Michele CirilloSimone ScardapaneSteven Van VaerenberghAurelio Uncini

In this brief we investigate the generalization properties of a recently-proposed class of non-parametric activation functions, the kernel activation functions (KAFs). KAFs introduce additional parameters in the learning process in order to adapt nonlinearities individually on a per-neuron basis, exploiting a cheap kernel expansion of every activation value... (read more)

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