130 citations · 134 across the 7 of their papers we have counts for
3 papers · 1 filter
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…