16 citations · 53 across the 28 of their papers we have counts for
28 papers
A Filtered Mixture-of-Generators for Fully Synthetic Survival Training
Niccolò Maria Rizzi, Eugenio Lomurno, Alberto Archetti +1
Survival analysis models time-to-event data, but in clinical settings training data are costly and scarce: events accrue over years of follow-up, cohorts are small, and privacy reg…
Inference-Time Refinement Closes the Synthetic-Real Gap in Tabular Diffusion
Eugenio Lomurno, Filippo Balzarini, Francesco Benelle +2
Diffusion-based generators set the current state of the art for synthetic tabular data. These methods approach but rarely exceed real-data utility, and closing this synthetic-real…
SNPgen: Phenotype-Supervised Genotype Representation and Synthetic Data Generation via Latent Diffusion
Andrea Lampis, Michela Carlotta Massi, Nicola Pirastu +3
Polygenic risk scores and other genomic analyses require large individual-level genotype datasets, yet strict data access restrictions impede sharing. Synthetic genotype generation…
SurvKAN: A Fully Parametric Survival Model Based on Kolmogorov-Arnold Networks
Marina Mastroleo, Alberto Archetti, Federico Mastroleo +1
Accurate prediction of time-to-event outcomes is critical for clinical decision-making, treatment planning, and resource allocation in modern healthcare. While classical survival m…
Deep Variational Contrastive Learning for Joint Risk Stratification and Time-to-Event Estimation
Pinar Erbil, Alberto Archetti, Eugenio Lomurno +1
Survival analysis is essential for clinical decision-making, as it allows practitioners to estimate time-to-event outcomes, stratify patient risk profiles, and guide treatment plan…
ZO-DARTS++: An Efficient and Size-Variable Zeroth-Order Neural Architecture Search Algorithm
Lunchen Xie, Eugenio Lomurno, Matteo Gambella +4
Differentiable Neural Architecture Search (NAS) provides a promising avenue for automating the complex design of deep learning (DL) models. However, current differentiable NAS meth…
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…
Age Group Discrimination via Free Handwriting Indicators
Eugenio Lomurno, Simone Toffoli, Davide Di Febbo +3
The growing global elderly population is expected to increase the prevalence of frailty, posing significant challenges to healthcare systems. Frailty, a syndrome associated with ag…
Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomics
Alberto Archetti, Francesca Ieva, Matteo Matteucci
Survival analysis is a fundamental tool in medicine, modeling the time until an event of interest occurs in a population. However, in real-world applications, survival data are oft…
Neural Architecture Transfer 2: A Paradigm for Improving Efficiency in Multi-Objective Neural Architecture Search
Simone Sarti, Eugenio Lomurno, Matteo Matteucci
Deep learning is increasingly impacting various aspects of contemporary society. Artificial neural networks have emerged as the dominant models for solving an expanding range of ta…
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…
Federated Survival Forests
Alberto Archetti, Matteo Matteucci
Survival analysis is a subfield of statistics concerned with modeling the occurrence time of a particular event of interest for a population. Survival analysis found widespread app…
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…