4 citations · 25 across the 15 of their papers we have counts for
8 papers · 1 filter
Schedule-Robust Online Continual Learning
Ruohan Wang, Marco Ciccone, Giulia Luise +3
A continual learning (CL) algorithm learns from a non-stationary data stream. The non-stationarity is modeled by some schedule that determines how data is presented over time. Most…
Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning
Donald Shenaj, Eros Fanì, Marco Toldo +6
Federated Learning (FL) has recently emerged as a possible way to tackle the domain shift in real-world Semantic Segmentation (SS) without compromising the private nature of the co…
A Marriage between Adversarial Team Games and 2-player Games: Enabling Abstractions, No-regret Learning, and Subgame Solving
Luca Carminati, Federico Cacciamani, Marco Ciccone +1
\emph{Ex ante} correlation is becoming the mainstream approach for \emph{sequential adversarial team games}, where a team of players faces another team in a zero-sum game. It is kn…
Fault-Aware Design and Training to Enhance DNNs Reliability with Zero-Overhead
Niccolò Cavagnero, Fernando Dos Santos, Marco Ciccone +3
Deep Neural Networks (DNNs) enable a wide series of technological advancements, ranging from clinical imaging, to predictive industrial maintenance and autonomous driving. However,…
Improving Generalization in Federated Learning by Seeking Flat Minima
Debora Caldarola, Barbara Caputo, Marco Ciccone
Models trained in federated settings often suffer from degraded performances and fail at generalizing, especially when facing heterogeneous scenarios. In this work, we investigate…
FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving
Lidia Fantauzzo, Eros Fanì, Debora Caldarola +4
Semantic Segmentation is essential to make self-driving vehicles autonomous, enabling them to understand their surroundings by assigning individual pixels to known categories. Howe…