320 citations · 740 across the 21 of their papers we have counts for
6 papers · 1 filter
A Gentle Introduction to Deep Learning for Graphs
Davide Bacciu, Federico Errica, Alessio Micheli +1
The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community. The snap inc…
A Fair Comparison of Graph Neural Networks for Graph Classification
Federico Errica, Marco Podda, Davide Bacciu +1
Experimental reproducibility and replicability are critical topics in machine learning. Authors have often raised concerns about their lack in scientific publications to improve th…
Fast and Deep Graph Neural Networks
Claudio Gallicchio, Alessio Micheli
We address the efficiency issue for the construction of a deep graph neural network (GNN). The approach exploits the idea of representing each input graph as a fixed point of a dyn…
Reservoir Topology in Deep Echo State Networks
Claudio Gallicchio, Alessio Micheli
Deep Echo State Networks (DeepESNs) recently extended the applicability of Reservoir Computing (RC) methods towards the field of deep learning. In this paper we study the impact of…
Embeddings and Representation Learning for Structured Data
Benjamin Paaßen, Claudio Gallicchio, Alessio Micheli +1
Performing machine learning on structured data is complicated by the fact that such data does not have vectorial form. Therefore, multiple approaches have emerged to construct vect…
Richness of Deep Echo State Network Dynamics
Claudio Gallicchio, Alessio Micheli
Reservoir Computing (RC) is a popular methodology for the efficient design of Recurrent Neural Networks (RNNs). Recently, the advantages of the RC approach have been extended to th…