| State of the art of visual analytics for explainable deep learning B La Rosa, G Blasilli, R Bourqui, D Auber, G Santucci, R Capobianco, ... Computer Graphics Forum 42 (1), 319-355, 2023 | 57 | 2023 |
| Prototype-based interpretable graph neural networks A Ragno, B La Rosa, R Capobianco IEEE Transactions on Artificial Intelligence 5 (4), 1486-1495, 2022 | 15 | 2022 |
| A self-interpretable module for deep image classification on small data B La Rosa, R Capobianco, D Nardi Applied Intelligence 53 (8), 9115-9147, 2023 | 10 | 2023 |
| Explainable inference on sequential data via memory-tracking B La Rosa, R Capobianco, D Nardi IJCAI, 2006-2013, 2021 | 10 | 2021 |
| Explainable AI in drug discovery: self-interpretable graph neural network for molecular property prediction using concept whitening M Proietti, A Ragno, BL Rosa, R Ragno, R Capobianco Machine Learning 113 (4), 2013-2044, 2024 | 7 | 2024 |
| Towards a fuller understanding of neurons with Clustered Compositional Explanations B La Rosa, LH Gilpin, R Capobianco Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS …, 2023 | 4 | 2023 |
| Detection accuracy for evaluating compositional explanations of units SM Makinwa, B La Rosa, R Capobianco International Conference of the Italian Association for Artificial …, 2021 | 2 | 2021 |
| A Discussion about Explainable Inference on Sequential Data via Memory-Tracking. B La Rosa, R Capobianco, D Nardi DP@ AI* IA, 33-44, 2021 | 1 | 2021 |
| Explaining deep neural networks by leveraging intrinsic methods B La Rosa arXiv preprint arXiv:2407.12243, 2024 | | 2024 |
Roberto CapobiancoSapienza University of Rome - Sony AIEmail verificata su diag.uniroma1.it
