3 citations · 5 across the 4 of their papers we have counts for
4 papers
A Framework for Evaluating Zero-Shot Image Generation in Concept-based Explainability
Giacomo Astolfi, Matteo Bianchi, Riccardo Campi +2
Concept-based Explainable Artificial Intelligence (XAI) interprets deep learning models using human-understandable visual features (e.g., textures or object parts) by linking inter…
MDER-DR: Multi-Hop Question Answering with Entity-Centric Summaries
Riccardo Campi, Nicolò Oreste Pinciroli Vago, Mathyas Giudici +2
Retrieval-Augmented Generation (RAG) over Knowledge Graphs (KGs) suffers from the fact that indexing approaches may lose important contextual nuance when text is reduced to triples…
A Graph-based RAG for Energy Efficiency Question Answering
Riccardo Campi, Nicolò Oreste Pinciroli Vago, Mathyas Giudici +3
In this work, we investigate the use of Large Language Models (LLMs) within a graph-based Retrieval Augmented Generation (RAG) architecture for Energy Efficiency (EE) Question Answ…
Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification
Antonio De Santis, Riccardo Campi, Matteo Bianchi +1
Convolutional Neural Networks (CNNs) have shown remarkable performance in image classification. However, interpreting their predictions is challenging due to the size and complexit…