6 citations · 19 across the 16 of their papers we have counts for
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Bridging the Gap: Enhancing the Utility of Synthetic Data via Post-Processing Techniques
Andrea Lampis, Eugenio Lomurno, Matteo Matteucci
Acquiring and annotating suitable datasets for training deep learning models is challenging. This often results in tedious and time-consuming efforts that can hinder research progr…
Discriminative Adversarial Privacy: Balancing Accuracy and Membership Privacy in Neural Networks
Eugenio Lomurno, Alberto Archetti, Francesca Ausonio +1
The remarkable proliferation of deep learning across various industries has underscored the importance of data privacy and security in AI pipelines. As the evolution of sophisticat…
Two Steps Forward and One Behind: Rethinking Time Series Forecasting with Deep Learning
Riccardo Ughi, Eugenio Lomurno, Matteo Matteucci
The Transformer is a highly successful deep learning model that has revolutionised the world of artificial neural networks, first in natural language processing and later in comput…
Heterogeneous Datasets for Federated Survival Analysis Simulation
Alberto Archetti, Eugenio Lomurno, Francesco Lattari +2
Survival analysis studies time-modeling techniques for an event of interest occurring for a population. Survival analysis found widespread applications in healthcare, engineering,…
Enhancing Once-For-All: A Study on Parallel Blocks, Skip Connections and Early Exits
Simone Sarti, Eugenio Lomurno, Andrea Falanti +1
The use of Neural Architecture Search (NAS) techniques to automate the design of neural networks has become increasingly popular in recent years. The proliferation of devices with…
Anticipate, Ensemble and Prune: Improving Convolutional Neural Networks via Aggregated Early Exits
Simone Sarti, Eugenio Lomurno, Matteo Matteucci
Today, artificial neural networks are the state of the art for solving a variety of complex tasks, especially in image classification. Such architectures consist of a sequence of s…