A Kernel Activation Function is a non-parametric activation function defined as a one-dimensional kernel approximator:
$$ f(s) = \sum_{i=1}^D \alpha_i \kappa( s, d_i) $$
where:
In addition, the linear coefficients can be initialized using kernel ridge regression to behave similarly to a known function in the beginning of the optimization process.
Source: Kafnets: kernel-based non-parametric activation functions for neural networksPaper | Code | Results | Date | Stars |
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