3 papers
cs.LG2026
Pairwise is Not Enough: Hypergraph Neural Networks for Multi-Agent Pathfinding
Rishabh Jain, Keisuke Okumura, Michael Amir +2
Multi-Agent Path Finding (MAPF) is a representative multi-agent coordination problem, where multiple agents are required to navigate to their respective goals without collisions. S…
cs.LG2025
EU-Nets: Enhanced, Explainable and Parsimonious U-Nets
B. Sun, P. Liò
In this study, we propose MHEX+, a framework adaptable to any U-Net architecture. Built upon MHEX+, we introduce novel U-Net variants, EU-Nets, which enhance explainability and unc…
cs.CV2025
Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers
Bohang Sun, Pietro Liò
In this study, we introduce the Multi-Head Explainer (MHEX), a versatile and modular framework that enhances both the explainability and accuracy of Convolutional Neural Networks (…