9 citations · 17 across the 10 of their papers we have counts for
10 papers
Triplètoile: Extraction of Knowledge from Microblogging Text
Vanni Zavarella, Sergio Consoli, Diego Reforgiato Recupero +5
Numerous methods and pipelines have recently emerged for the automatic extraction of knowledge graphs from documents such as scientific publications and patents. However, adapting…
Fair Augmentation for Graph Collaborative Filtering
Ludovico Boratto, Francesco Fabbri, Gianni Fenu +2
Recent developments in recommendation have harnessed the collaborative power of graph neural networks (GNNs) in learning users' preferences from user-item networks. Despite emergin…
If It's Not Enough, Make It So: Reducing Authentic Data Demand in Face Recognition through Synthetic Faces
Andrea Atzori, Fadi Boutros, Naser Damer +2
Recent advances in deep face recognition have spurred a growing demand for large, diverse, and manually annotated face datasets. Acquiring authentic, high-quality data for face rec…
Robustness in Fairness against Edge-level Perturbations in GNN-based Recommendation
Ludovico Boratto, Francesco Fabbri, Gianni Fenu +2
Efforts in the recommendation community are shifting from the sole emphasis on utility to considering beyond-utility factors, such as fairness and robustness. Robustness of recomme…
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Pietro Melzi, Ruben Tolosana, Ruben Vera-Rodriguez +44
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must…
Counterfactual Graph Augmentation for Consumer Unfairness Mitigation in Recommender Systems
Ludovico Boratto, Francesco Fabbri, Gianni Fenu +2
In recommendation literature, explainability and fairness are becoming two prominent perspectives to consider. However, prior works have mostly addressed them separately, for insta…