3 citations · 4 across the 7 of their papers we have counts for
7 papers
Beyond single-model XAI: aggregating multi-model explanations for enhanced trustworthiness
Ilaria Vascotto, Alex Rodriguez, Alessandro Bonaita +1
The use of Artificial Intelligence (AI) models in real-world and high-risk applications has intensified the discussion about their trustworthiness and ethical usage, from both a te…
Assessing reliability of explanations in unbalanced datasets: a use-case on the occurrence of frost events
Ilaria Vascotto, Valentina Blasone, Alex Rodriguez +2
The usage of eXplainable Artificial Intelligence (XAI) methods has become essential in practical applications, given the increasing deployment of Artificial Intelligence (AI) model…
Intrinsic Dimension Correlation: uncovering nonlinear connections in multimodal representations
Lorenzo Basile, Santiago Acevedo, Luca Bortolussi +2
To gain insight into the mechanisms behind machine learning methods, it is crucial to establish connections among the features describing data points. However, these correlations o…
When Can You Trust Your Explanations? A Robustness Analysis on Feature Importances
Ilaria Vascotto, Alex Rodriguez, Alessandro Bonaita +1
Recent legislative regulations have underlined the need for accountable and transparent artificial intelligence systems and have contributed to a growing interest in the Explainabl…
Machine Learning Catalysis of Quantum Tunneling
Renzo Testa, Alex Rodriguez, Alberto d'Onofrio +3
Optimizing the probability of quantum tunneling between two states, while keeping the resources of the underlying physical system constant, is a task of key importance due to its c…
Catalysis of quantum tunneling by ancillary system learning
Renzo Testa, Alex Rodriguez, Alberto d'Onofrio +3
Given the key role that quantum tunneling plays in a wide range of applications, a crucial objective is to maximize the probability of tunneling from one quantum state/level to ano…
Frequency maps reveal the correlation between Adversarial Attacks and Implicit Bias
Lorenzo Basile, Nikos Karantzas, Alberto d'Onofrio +4
Despite their impressive performance in classification tasks, neural networks are known to be vulnerable to adversarial attacks, subtle perturbations of the input data designed to…