1 citations · 2 across the 10 of their papers we have counts for
13 papers
Graph Additive Models disentangling feature, topology, and interactions
Clara Alvarez-Rodriguez, Steve Azzolin, Vincenzo Marco De Luca +3
Graph Neural Additive Networks (GNANs) have emerged as effective, interpretable-by-design architectures for graph learning. They achieve transparency by combining per-feature shape…
Reachability is not enough: Diagnosing long-range behavior in GNNs
Filippo Maria Bianchi
Graph neural networks (GNNs) are often called long-range because their architecture can connect distant nodes, but this does not show whether they use distant information correctly…
Data-driven Prediction of Satellite-observed Avalanche Activity from Snowpack Simulations
Jakob Grahn, Jakob Grah, Filippo Maria Bianchi +2
Avalanche forecasting requires knowledge of snowpack conditions and recent avalanche activity, but field observations are sparse across large mountain regions. We explore whether S…
Weakly Supervised Polar Low Segmentation in Sentinel-1 SAR Imagery
Andrea Federici, Jakob Grahn, Giacomo Boracchi +1
Polar lows are intense maritime cyclones that form rapidly at high latitudes. Deep learning can detect them in Synthetic Aperture Radar (SAR) imagery, but pixel-level segmentation…
Hierarchical Pooling for Sheaf Neural Networks
Dionisia Naddeo, Carlo Abate, Pietro Liò +2
Sheaf Neural Networks (SNNs) generalize Graph Neural Networks (GNNs) by replacing scalar node signals with stalk-valued signals and by using restriction maps to measure compatibili…
A Generalized Tikhonov Layer for Interpretable-by-design Graph Neural Networks
Nicolas Tremblay, Benjamin Ricaud, Filippo Maria Bianchi
We propose the Tikhonov layer, a graph neural network layer that is interpretable by design: once trained, its learned parameters directly reveal which node features and which aspe…
Promptable Foundation Models for SAR Remote Sensing: Adapting the Segment Anything Model for Snow Avalanche Segmentation
Riccardo Gelato, Carlo Sgaravatti, Jakob Grahn +2
Remote sensing solutions for avalanche segmentation and mapping are key to supporting risk forecasting and mitigation in mountain regions. Synthetic Aperture Radar (SAR) imagery fr…
Torch Geometric Pool: the PyTorch library for pooling in Graph Neural Networks
Carlo Abate, Ivan Marisca, Filippo Maria Bianchi
Torch Geometric Pool (tgp) is a pooling library built on top of PyTorch Geometric. Graph pooling methods differ in how they assign nodes to supernodes, how they handle batches, wha…
ResCP: Reservoir Conformal Prediction for Time Series Forecasting
Roberto Neglia, Andrea Cini, Michael M. Bronstein +1
Conformal prediction offers a powerful framework for building distribution-free prediction intervals for exchangeable data. Existing methods that extend conformal prediction to seq…
Monitoring snow avalanches from SAR data with deep learning
Filippo Maria Bianchi, Jakob Grahn
Snow avalanches present significant risks to human life and infrastructure, particularly in mountainous regions, making effective monitoring crucial. Traditional monitoring methods…
Relational Conformal Prediction for Correlated Time Series
Andrea Cini, Alexander Jenkins, Danilo Mandic +2
We address the problem of uncertainty quantification in time series forecasting by exploiting observations at correlated sequences. Relational deep learning methods leveraging grap…
BN-Pool: Bayesian Nonparametric Pooling for Graphs
Daniele Castellana, Filippo Maria Bianchi
We introduce BN-Pool, the first clustering-based pooling method for Graph Neural Networks that adaptively determines the number of supernodes in a coarsened graph. BN-Pool leverage…
MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks
Carlo Abate, Filippo Maria Bianchi
We propose a novel approach to compute the MAXCUT in attributed graphs, i.e., graphs with features associated with nodes and edges. Our approach works well on any kind of graph top…