2 citations · 3 across the 4 of their papers we have counts for
7 papers
A Classification of Artificial Intelligence Systems for Mathematics Education
Steven Van Vaerenbergh, Adrián Pérez-Suay
This chapter provides an overview of the different Artificial Intelligence (AI) systems that are being used in contemporary digital tools for Mathematics Education (ME). It is aime…
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…
Recurrent Neural Networks with Flexible Gates using Kernel Activation Functions
Simone Scardapane, Steven Van Vaerenbergh, Danilo Comminiello +2
Gated recurrent neural networks have achieved remarkable results in the analysis of sequential data. Inside these networks, gates are used to control the flow of information, allow…
Improving Graph Convolutional Networks with Non-Parametric Activation Functions
Simone Scardapane, Steven Van Vaerenbergh, Danilo Comminiello +1
Graph neural networks (GNNs) are a class of neural networks that allow to efficiently perform inference on data that is associated to a graph structure, such as, e.g., citation net…
Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space
Steven Van Vaerenbergh, Ignacio Santamaria, Victor Elvira +1
In this paper, we study the problem of locating a predefined sequence of patterns in a time series. In particular, the studied scenario assumes a theoretical model is available tha…