3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.CV2026
Scaling Vision Models Does Not Consistently Improve Localisation-Based Explanation Quality
Mateusz Cedro, Marcin Chlebus
Artificial intelligence models are increasingly scaled to improve predictive accuracy, yet it remains unclear whether scale improves the quality of post-hoc explanations. We invest…
cs.CV2024★ 1 cited
Beyond the Black Box: Do More Complex Deep Learning Models Provide Superior XAI Explanations?
Mateusz Cedro, Marcin Chlebus
The increasing complexity of Artificial Intelligence models poses challenges to interpretability, particularly in the healthcare sector. This study investigates the impact of deep…
q-fin.RM2021★ 3 cited
Enabling Machine Learning Algorithms for Credit Scoring -- Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models
Przemysław Biecek, Marcin Chlebus, Janusz Gajda +5
Rapid development of advanced modelling techniques gives an opportunity to develop tools that are more and more accurate. However as usually, everything comes with a price and in t…