130 citations · 134 across the 7 of their papers we have counts for
3 papers · 1 filter
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…
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…
Overlapping Mixtures of Gaussian Processes for the Data Association Problem
Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Neil Lawrence
In this work we introduce a mixture of GPs to address the data association problem, i.e. to label a group of observations according to the sources that generated them. Unlike sever…