2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.AI2023★ 2 cited
Explaining and visualizing black-box models through counterfactual paths
Bastian Pfeifer, Mateusz Krzyzinski, Hubert Baniecki +3
Explainable AI (XAI) is an increasingly important area of machine learning research, which aims to make black-box models transparent and interpretable. In this paper, we propose a…
cs.CL2023
HADES: Homologous Automated Document Exploration and Summarization
Piotr Wilczyński, Artur Żółkowski, Mateusz Krzyziński +5
This paper introduces HADES, a novel tool for automatic comparative documents with similar structures. HADES is designed to streamline the work of professionals dealing with large…