6 citations · 19 across the 16 of their papers we have counts for
16 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…
PolyGen: Fully Synthetic Vision-Language Training via Multi-Generator Ensembles
Leonardo Brusini, Cristian Sbrolli, Eugenio Lomurno +2
Synthetic data offers a scalable solution for vision-language pre-training, yet current state-of-the-art methods typically rely on scaling up a single generative backbone, which in…
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…
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…
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…
POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification
Andrea Falanti, Eugenio Lomurno, Danilo Ardagna +1
The automated machine learning (AutoML) field has become increasingly relevant in recent years. These algorithms can develop models without the need for expert knowledge, facilitat…
POPNASv2: An Efficient Multi-Objective Neural Architecture Search Technique
Andrea Falanti, Eugenio Lomurno, Stefano Samele +2
Automating the research for the best neural network model is a task that has gained more and more relevance in the last few years. In this context, Neural Architecture Search (NAS)…
On the utility and protection of optimization with differential privacy and classic regularization techniques
Eugenio Lomurno, Matteo matteucci
Nowadays, owners and developers of deep learning models must consider stringent privacy-preservation rules of their training data, usually crowd-sourced and retaining sensitive inf…
SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning
Eugenio Lomurno, Alberto Archetti, Lorenzo Cazzella +3
Privacy regulation laws, such as GDPR, impose transparency and security as design pillars for data processing algorithms. In this context, federated learning is one of the most inf…
Improving Multi-View Stereo via Super-Resolution
Eugenio Lomurno, Andrea Romanoni, Matteo Matteucci
Today, Multi-View Stereo techniques are able to reconstruct robust and detailed 3D models, especially when starting from high-resolution images. However, there are cases in which t…