3 papers
cs.CV2024
Adaptive Point Transformer
Alessandro Baiocchi, Indro Spinelli, Alessandro Nicolosi +1
The recent surge in 3D data acquisition has spurred the development of geometric deep learning models for point cloud processing, boosted by the remarkable success of transformers…
cs.LG2023
Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability
Indro Spinelli, Michele Guerra, Filippo Maria Bianchi +1
Subgraph-enhanced graph neural networks (SGNN) can increase the expressive power of the standard message-passing framework. This model family represents each graph as a collection…
cs.LG2023
Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks
Indro Spinelli, Riccardo Bianchini, Simone Scardapane
The rise of graph representation learning as the primary solution for many different network science tasks led to a surge of interest in the fairness of this family of methods. Lin…