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…
A multi-stage GAN for multi-organ chest X-ray image generation and segmentation
Giorgio Ciano, Paolo Andreini, Tommaso Mazzierli +2
Multi-organ segmentation of X-ray images is of fundamental importance for computer aided diagnosis systems. However, the most advanced semantic segmentation methods rely on deep le…
Molecular graph generation with Graph Neural Networks
Pietro Bongini, Monica Bianchini, Franco Scarselli
Drug Discovery is a fundamental and ever-evolving field of research. The design of new candidate molecules requires large amounts of time and money, and computational methods are b…
Weak Supervision for Generating Pixel-Level Annotations in Scene Text Segmentation
Simone Bonechi, Paolo Andreini, Monica Bianchini +1
Providing pixel-level supervisions for scene text segmentation is inherently difficult and costly, so that only few small datasets are available for this task. To face the scarcity…
A Two Stage GAN for High Resolution Retinal Image Generation and Segmentation
Paolo Andreini, Simone Bonechi, Monica Bianchini +3
In recent years, the use of deep learning is becoming increasingly popular in computer vision. However, the effective training of deep architectures usually relies on huge sets of…
Modeling Taxi Drivers' Behaviour for the Next Destination Prediction
Alberto Rossi, Gianni Barlacchi, Monica Bianchini +1
In this paper, we study how to model taxi drivers' behaviour and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey…