32 citations · 170 across the 50 of their papers we have counts for
7 papers · 2 filters
A Gentle Introduction to Deep Learning for Graphs
Davide Bacciu, Federico Errica, Alessio Micheli +1
The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community. The snap inc…
A Fair Comparison of Graph Neural Networks for Graph Classification
Federico Errica, Marco Podda, Davide Bacciu +1
Experimental reproducibility and replicability are critical topics in machine learning. Authors have often raised concerns about their lack in scientific publications to improve th…
A Non-Negative Factorization approach to node pooling in Graph Convolutional Neural Networks
Davide Bacciu, Luigi Di Sotto
The paper discusses a pooling mechanism to induce subsampling in graph structured data and introduces it as a component of a graph convolutional neural network. The pooling mechani…
Bayesian Tensor Factorisation for Bottom-up Hidden Tree Markov Models
Daniele Castellana, Davide Bacciu
Bottom-Up Hidden Tree Markov Model is a highly expressive model for tree-structured data. Unfortunately, it cannot be used in practice due to the intractable size of its state-tran…
Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI)
Elisa Ferrari, Alessandra Retico, Davide Bacciu
Over the years, there has been growing interest in using Machine Learning techniques for biomedical data processing. When tackling these tasks, one needs to bear in mind that biome…
Detecting Adversarial Examples through Nonlinear Dimensionality Reduction
Francesco Crecchi, Davide Bacciu, Battista Biggio
Deep neural networks are vulnerable to adversarial examples, i.e., carefully-perturbed inputs aimed to mislead classification. This work proposes a detection method based on combin…