439 citations · 1.4k across the 66 of their papers we have counts for
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A Graph Deep Learning Framework for High-Level Synthesis Design Space Exploration
Lorenzo Ferretti, Andrea Cini, Georgios Zacharopoulos +2
The design of efficient hardware accelerators for high-throughput data-processing applications, e.g., deep neural networks, is a challenging task in computer architecture design. I…
Graph neural network-based fault diagnosis: a review
Zhiwen Chen, Jiamin Xu, Cesare Alippi +4
Graph neural network (GNN)-based fault diagnosis (FD) has received increasing attention in recent years, due to the fact that data coming from several application domains can be ad…
Learning Graph Cellular Automata
Daniele Grattarola, Lorenzo Livi, Cesare Alippi
Cellular automata (CA) are a class of computational models that exhibit rich dynamics emerging from the local interaction of cells arranged in a regular lattice. In this work we fo…
Understanding Pooling in Graph Neural Networks
Daniele Grattarola, Daniele Zambon, Filippo Maria Bianchi +1
Inspired by the conventional pooling layers in convolutional neural networks, many recent works in the field of graph machine learning have introduced pooling operators to reduce t…
Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks
Andrea Cini, Ivan Marisca, Cesare Alippi
Dealing with missing values and incomplete time series is a labor-intensive, tedious, inevitable task when handling data coming from real-world applications. Effective spatio-tempo…