16 citations · 53 across the 28 of their papers we have counts for
8 papers · 1 filter
POMONAG: Pareto-Optimal Many-Objective Neural Architecture Generator
Eugenio Lomurno, Samuele Mariani, Matteo Monti +1
Neural Architecture Search (NAS) automates neural network design, reducing dependence on human expertise. While NAS methods are computationally intensive and dataset-specific, auxi…
FPBoost: Fully Parametric Gradient Boosting for Survival Analysis
Alberto Archetti, Eugenio Lomurno, Diego Piccinotti +1
Survival analysis is a statistical framework for modeling time-to-event data. It plays a pivotal role in medicine, reliability engineering, and social science research, where under…
Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharing
Eugenio Lomurno, Matteo Matteucci
Federated learning has emerged as a paradigm for collaborative learning, enabling the development of robust models without the need to centralise sensitive data. However, conventio…
Synthetic Image Learning: Preserving Performance and Preventing Membership Inference Attacks
Eugenio Lomurno, Matteo Matteucci
Generative artificial intelligence has transformed the generation of synthetic data, providing innovative solutions to challenges like data scarcity and privacy, which are particul…
A Lightweight Neural Architecture Search Model for Medical Image Classification
Lunchen Xie, Eugenio Lomurno, Matteo Gambella +4
Accurate classification of medical images is essential for modern diagnostics. Deep learning advancements led clinicians to increasingly use sophisticated models to make faster and…
Stable Diffusion Dataset Generation for Downstream Classification Tasks
Eugenio Lomurno, Matteo D'Oria, Matteo Matteucci
Recent advances in generative artificial intelligence have enabled the creation of high-quality synthetic data that closely mimics real-world data. This paper explores the adaptati…
Latent Neural Cellular Automata for Resource-Efficient Image Restoration
Andrea Menta, Alberto Archetti, Matteo Matteucci
Neural cellular automata represent an evolution of the traditional cellular automata model, enhanced by the integration of a deep learning-based transition function. This shift fro…
Harnessing the Computing Continuum across Personalized Healthcare, Maintenance and Inspection, and Farming 4.0
Fatemeh Baghdadi, Davide Cirillo, Daniele Lezzi +6
The AI-SPRINT project, launched in 2021 and funded by the European Commission, focuses on the development and implementation of AI applications across the computing continuum. This…