Search Results for author: Eugenio Lomurno

Found 14 papers, 1 papers with code

Harnessing the Computing Continuum across Personalized Healthcare, Maintenance and Inspection, and Farming 4.0

no code implementations23 Feb 2024 Fatemeh Baghdadi, Davide Cirillo, Daniele Lezzi, Francesc Lordan, Fernando Vazquez, Eugenio Lomurno, Alberto Archetti, Danilo Ardagna, Matteo Matteucci

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.

Age Group Discrimination via Free Handwriting Indicators

no code implementations29 Sep 2023 Eugenio Lomurno, Simone Toffoli, Davide Di Febbo, Matteo Matteucci, Francesca Lunardini, Simona Ferrante

Content-free handwriting data from 80 healthy participants in different age groups (20-40, 41-60, 61-70 and 70+) were analysed.

Discriminative Adversarial Privacy: Balancing Accuracy and Membership Privacy in Neural Networks

no code implementations5 Jun 2023 Eugenio Lomurno, Alberto Archetti, Francesca Ausonio, Matteo Matteucci

The remarkable proliferation of deep learning across various industries has underscored the importance of data privacy and security in AI pipelines.

Bridging the Gap: Enhancing the Utility of Synthetic Data via Post-Processing Techniques

no code implementations17 May 2023 Andrea Lampis, Eugenio Lomurno, Matteo Matteucci

These results represent a new state of the art in Classification Accuracy Score and highlight the effectiveness of post-processing techniques in improving the quality of synthetic datasets.

Two Steps Forward and One Behind: Rethinking Time Series Forecasting with Deep Learning

no code implementations10 Apr 2023 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 computer vision.

Time Series Time Series Forecasting

Enhancing Once-For-All: A Study on Parallel Blocks, Skip Connections and Early Exits

no code implementations3 Feb 2023 Simone Sarti, Eugenio Lomurno, Andrea Falanti, Matteo Matteucci

The use of Neural Architecture Search (NAS) techniques to automate the design of neural networks has become increasingly popular in recent years.

Knowledge Distillation Neural Architecture Search

Anticipate, Ensemble and Prune: Improving Convolutional Neural Networks via Aggregated Early Exits

no code implementations28 Jan 2023 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.

Edge-computing Image Classification

POPNASv2: An Efficient Multi-Objective Neural Architecture Search Technique

no code implementations6 Oct 2022 Andrea Falanti, Eugenio Lomurno, Stefano Samele, Danilo Ardagna, Matteo Matteucci

With its sequential model-based optimization strategy, Progressive Neural Architecture Search (PNAS) represents a possible step forward to face this resources issue.

Neural Architecture Search

On the utility and protection of optimization with differential privacy and classic regularization techniques

no code implementations7 Sep 2022 Eugenio Lomurno, Matteo Matteucci

According to the literature, this approach has proven to be a successful defence against several models' privacy attacks, but its downside is a substantial degradation of the models' performance.

L2 Regularization Privacy Preserving

SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning

no code implementations24 Sep 2021 Eugenio Lomurno, Alberto Archetti, Lorenzo Cazzella, Stefano Samele, Leonardo Di Perna, Matteo Matteucci

In this context, federated learning is one of the most influential frameworks for privacy-preserving distributed machine learning, achieving astounding results in many natural language processing and computer vision tasks.

BIG-bench Machine Learning Fairness +2

Improving Multi-View Stereo via Super-Resolution

no code implementations28 Jul 2021 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.

Super-Resolution

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