5 papers
Random-Set Graph Neural Networks
Tommy Woodley, Shireen Kudukkil Manchingal, Matteo Tolloso +2
Uncertainty quantification has become an important factor in understanding the data representations produced by Graph Neural Networks (GNNs). Despite their predictive capabilities…
Can You Hear Me Now? A Benchmark for Long-Range Graph Propagation
Luca Miglior, Matteo Tolloso, Alessio Gravina +1
Effectively capturing long-range interactions remains a fundamental yet unresolved challenge in graph neural network (GNN) research, critical for applications across diverse fields…
Boundary-Constrained Diffusion Models for Floorplan Generation: Balancing Realism and Diversity
Leonardo Stoppani, Davide Bacciu, Shahab Mokarizadeh
Diffusion models have become widely popular for automated floorplan generation, producing highly realistic layouts conditioned on user-defined constraints. However, optimizing for…
Credal Graph Neural Networks
Matteo Tolloso, Davide Bacciu
Uncertainty quantification is essential for deploying reliable Graph Neural Networks (GNNs), where existing approaches primarily rely on Bayesian inference or ensembles. In this pa…
Real-time and personalized product recommendations for large e-commerce platforms
Matteo Tolloso, Davide Bacciu, Shahab Mokarizadeh +1
We present a methodology to provide real-time and personalized product recommendations for large e-commerce platforms, specifically focusing on fashion retail. Our approach aims to…