477 citations · 479 across the 4 of their papers we have counts for
4 papers
On the Stability and Generalization of Learning with Kernel Activation Functions
Michele Cirillo, Simone Scardapane, Steven Van Vaerenbergh +1
In this brief we investigate the generalization properties of a recently-proposed class of non-parametric activation functions, the kernel activation functions (KAFs). KAFs introdu…
Widely Linear Kernels for Complex-Valued Kernel Activation Functions
Simone Scardapane, Steven Van Vaerenbergh, Danilo Comminiello +1
Complex-valued neural networks (CVNNs) have been shown to be powerful nonlinear approximators when the input data can be properly modeled in the complex domain. One of the major ch…
Kafnets: kernel-based non-parametric activation functions for neural networks
Simone Scardapane, Steven Van Vaerenbergh, Simone Totaro +1
Neural networks are generally built by interleaving (adaptable) linear layers with (fixed) nonlinear activation functions. To increase their flexibility, several authors have propo…
Group Sparse Regularization for Deep Neural Networks
Simone Scardapane, Danilo Comminiello, Amir Hussain +1
In this paper, we consider the joint task of simultaneously optimizing (i) the weights of a deep neural network, (ii) the number of neurons for each hidden layer, and (iii) the sub…