41 citations · 71 across the 20 of their papers we have counts for
5 papers · 1 filter
Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified by Other Networks
Simone Marullo, Matteo Tiezzi, Marco Gori +1
Amongst a variety of approaches aimed at making the learning procedure of neural networks more effective, the scientific community developed strategies to order the examples accord…
Messing Up 3D Virtual Environments: Transferable Adversarial 3D Objects
Enrico Meloni, Matteo Tiezzi, Luca Pasqualini +2
In the last few years, the scientific community showed a remarkable and increasing interest towards 3D Virtual Environments, training and testing Machine Learning-based models in r…
Evaluating Continual Learning Algorithms by Generating 3D Virtual Environments
Enrico Meloni, Alessandro Betti, Lapo Faggi +3
Continual learning refers to the ability of humans and animals to incrementally learn over time in a given environment. Trying to simulate this learning process in machines is a ch…
Graph Neural Networks for Graph Drawing
Matteo Tiezzi, Gabriele Ciravegna, Marco Gori
Graph Drawing techniques have been developed in the last few years with the purpose of producing aesthetically pleasing node-link layouts. Recently, the employment of differentiabl…
Friendly Training: Neural Networks Can Adapt Data To Make Learning Easier
Simone Marullo, Matteo Tiezzi, Marco Gori +1
In the last decade, motivated by the success of Deep Learning, the scientific community proposed several approaches to make the learning procedure of Neural Networks more effective…