From the 1 of 6 linked papers with an AI index.
6 papers
Who Are You Explaining To? A Multi-Agent System for Audience-Aware XAI Narratives
Francesco Musicco, Danilo Danese, Giuseppe Fasano +3
Feature-attribution methods such as SHAP provide useful evidence about individual model predictions, but their numerical outputs are rarely sufficient for audiences with different…
Now You Have My Healthy Attention: A U-DiT for Brain-MRI Inpainting
Danilo Danese, Angela Lombardi, Tommaso Di Noia
The paper presents a U-shaped transformer (U-DiT) model for inpainting healthy brain tissue in T1-weighted MRI scans, using constrained self‑attention and a contralateral‑symmetry…
FlowLet: Conditional 3D Brain MRI Synthesis using Wavelet Flow Matching
Danilo Danese, Angela Lombardi, Matteo Attimonelli +2
Brain Magnetic Resonance Imaging (MRI) plays a central role in studying neurological development, aging, and diseases. One key application is Brain Age Prediction (BAP), which esti…
WaveDiT: Distribution-Aware Wavelet Flow Matching for Efficient 3D Brain MRI Synthesis
Danilo Danese, Angela Lombardi, Giuseppe Fasano +2
Large and demographically balanced datasets are essential for reliable neuroimaging biomarkers. Full-resolution 3D brain MRI synthesis can support data augmentation in this setting…
Large-scale Benchmarks for Multimodal Recommendation with Ducho
Matteo Attimonelli, Danilo Danese, Angela Di Fazio +3
The common multimodal recommendation pipeline involves (i) extracting multimodal features, (ii) refining their high-level representations to suit the recommendation task, (iii) opt…
Do Recommender Systems Really Leverage Multimodal Content? A Comprehensive Analysis on Multimodal Representations for Recommendation
Claudio Pomo, Matteo Attimonelli, Danilo Danese +2
Multimodal Recommender Systems aim to improve recommendation accuracy by integrating heterogeneous content, such as images and textual metadata. While effective, it remains unclear…