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cs.LG2025
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients
Evandro S. Ortigossa, Fábio F. Dias, Brian Barr +2
The development of machine learning applications has increased significantly in recent years, motivated by the remarkable ability of learning-powered systems to discover and genera…
cs.LG2024
MOUNTAINEER: Topology-Driven Visual Analytics for Comparing Local Explanations
Parikshit Solunke, Vitoria Guardieiro, Joao Rulff +5
With the increasing use of black-box Machine Learning (ML) techniques in critical applications, there is a growing demand for methods that can provide transparency and accountabili…