16 citations · 23 across the 9 of their papers we have counts for
9 papers
QUORUM: QUality-Optimized Routing Using Multiple annotators
Antonio Purificato, Maria Sofia Bucarelli, Andrea Bacciu +2
Data annotation remains a central bottleneck in natural language processing, requiring human effort to obtain high-quality labels at scale. While Large Language Models (LLMs) offer…
Art Beyond Semantics: Sheaf-Informed Contrastive Learning for Multi-Relational Representations
Ludovica Schaerf, Antonio Purificato, Piera Riccio +2
Understanding a painting is never a single act. Art historians may analyze the same work through concepts of style, iconography, or historical context, dimensions that are not inte…
Select, Label, Evaluate: Active Testing in NLP
Antonio Purificato, Maria Sofia Bucarelli, Andrea Bacciu +2
Human annotation cost and time remain significant bottlenecks in Natural Language Processing (NLP), with test data annotation being particularly expensive due to the stringent requ…
Directional Sheaf Hypergraph Networks: Unifying Learning on Directed and Undirected Hypergraphs
Emanuele Mule, Stefano Fiorini, Antonio Purificato +3
Hypergraphs provide a natural way to represent higher-order interactions among multiple entities. While undirected hypergraphs have been extensively studied, the case of directed h…
One Search Fits All: Pareto-Optimal Eco-Friendly Model Selection
Filippo Betello, Antonio Purificato, Vittoria Vineis +2
The environmental impact of Artificial Intelligence (AI) is emerging as a significant global concern, particularly regarding model training. In this paper, we introduce GREEN (Guid…
The Majority Vote Paradigm Shift: When Popular Meets Optimal
Antonio Purificato, Maria Sofia Bucarelli, Anil Kumar Nelakanti +3
Reliably labelling data typically requires annotations from multiple human workers. However, humans are far from being perfect. Hence, it is a common practice to aggregate labels g…
Eco-Aware Graph Neural Networks for Sustainable Recommendations
Antonio Purificato, Fabrizio Silvestri
Recommender systems play a crucial role in alleviating information overload by providing personalized recommendations tailored to users' preferences and interests. Recently, Graph…
A Reproducible Analysis of Sequential Recommender Systems
Filippo Betello, Antonio Purificato, Federico Siciliano +4
Sequential Recommender Systems (SRSs) have emerged as a highly efficient approach to recommendation systems. By leveraging sequential data, SRSs can identify temporal patterns in u…
Sheaf4Rec: Sheaf Neural Networks for Graph-based Recommender Systems
Antonio Purificato, Giulia Cassarà, Federico Siciliano +2
Recent advancements in Graph Neural Networks (GNN) have facilitated their widespread adoption in various applications, including recommendation systems. GNNs have proven to be effe…