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View all- Andringa JBaptista MSantos B(2025)Counterfactual explanations for remaining useful life estimation within a Bayesian frameworkInformation Fusion10.1016/j.inffus.2025.102972118(102972)Online publication date: Jun-2025
Counterfactual explanations have emerged as a popular solution for the eXplainable AI (XAI) problem of elucidating the predictions of black-box deep-learning systems because people easily understand them, they apply across different problem ...
Deep learning has shown powerful performances in many fields, however its black-box nature hinders its further applications. In response, explainable artificial intelligence emerges, aiming to explain the predictions and behaviors of deep learning ...
By providing explanations for users and system designers to facilitate better understanding and decision making, explainable recommendation has been an important research problem. In this paper, we propose Counterfactual Explainable Recommendation (...
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