Author

Filippo Maria Bianchi

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#machine learning Preprint Oct 2026

Graph Additive Models disentangling feature, topology, and interactions

Graph Neural Additive Networks (GNANs) have emerged as effective, interpretable-by-design architectures for graph learning. They achieve transparency by combining per-feature shape functions with simple distance-based reweighting. However, GNANs have three limitations. First, node-wise relevance scores aggregate inform...

C. Álvarez-Rodríguez, Steve Azzolin, Vincenzo Marco De Luca et al. · 0 citations

#machine learning Preprint Sep 2026

Reachability is not enough: Diagnosing long-range behavior in GNNs

The results show that long-range capability depends on learning to use information at the distances required by the task and preserving that use during computation.

Filippo Maria Bianchi · 0 citations

#machine learning Open access Sep 2026

DATA-DRIVEN PREDICTION OF SATELLITE-OBSERVED AVALANCHE ACTIVITY FROM SNOWPACK SIMULATIONS

Avalanche forecasting requires knowledge of snowpack conditions and recent avalanche activity, but field observations are sparse across large mountain regions. We explore whether SNOWPACK simulations can predict avalanche activity mapped by synthetic aperture radar (SAR). We compiled five winters of Sentinel-1 avalanch...

Jakob Grahn, Filippo Maria Bianchi, Bert Kruyt et al. · Proceedings of the Internati... · 0 citations