179 citations · 306 across the 11 of their papers we have counts for
11 papers
APEX: Assumption-free Projection-based Embedding eXamination Metric for Generative Distribution Evaluation
Caterina Gallegati, Monica Bianchini, Franco Scarselli +2
As generative models achieve unprecedented visual quality, the gold standard for image evaluation remains traditional feature-distribution metrics. However, these metrics are prova…
A topological description of loss surfaces based on Betti Numbers
Maria Sofia Bucarelli, Giuseppe Alessio D'Inverno, Monica Bianchini +2
In the context of deep learning models, attention has recently been paid to studying the surface of the loss function in order to better understand training with methods based on g…
VC dimension of Graph Neural Networks with Pfaffian activation functions
Giuseppe Alessio D'Inverno, Monica Bianchini, Franco Scarselli
Graph Neural Networks (GNNs) have emerged in recent years as a powerful tool to learn tasks across a wide range of graph domains in a data-driven fashion; based on a message passin…
Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities
Antonio Longa, Veronica Lachi, Gabriele Santin +5
Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data. However, many real-world systems are dynamic in nature, since the grap…
Modular multi-source prediction of drug side-effects with DruGNN
Pietro Bongini, Franco Scarselli, Monica Bianchini +3
Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. Predicting the probability of side-effects, before their occurrence,…
On the approximation capability of GNNs in node classification/regression tasks
Giuseppe Alessio D'Inverno, Monica Bianchini, Maria Lucia Sampoli +1
Graph Neural Networks (GNNs) are a broad class of connectionist models for graph processing. Recent studies have shown that GNNs can approximate any function on graphs, modulo the…