121 citations · 338 across the 25 of their papers we have counts for
5 papers · 1 filter
SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligence
Viktar Dubovik, Łukasz Struski, Jacek Tabor +1
Understanding the decisions made by deep neural networks is essential in high-stakes domains such as medical imaging and autonomous driving. Yet, these models often lack transparen…
Personalized Interpretability -- Interactive Alignment of Prototypical Parts Networks
Tomasz Michalski, Adam Wróbel, Andrea Bontempelli +6
Concept-based interpretable neural networks have gained significant attention due to their intuitive and easy-to-understand explanations based on case-based reasoning, such as "thi…
B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data
Magdalena Proszewska, Tomasz Danel, Dawid Rymarczyk
Understanding the reasoning behind deep learning model predictions is crucial in cheminformatics and drug discovery, where molecular design determines their properties. However, cu…
AI-Driven Rapid Identification of Bacterial and Fungal Pathogens in Blood Smears of Septic Patients
Agnieszka Sroka-Oleksiak, Adam Pardyl, Dawid Rymarczyk +10
Sepsis is a life-threatening condition which requires rapid diagnosis and treatment. Traditional microbiological methods are time-consuming and expensive. In response to these chal…
SEMU: Singular Value Decomposition for Efficient Machine Unlearning
Marcin Sendera, Łukasz Struski, Kamil Książek +3
While the capabilities of generative foundational models have advanced rapidly in recent years, methods to prevent harmful and unsafe behaviors remain underdeveloped. Among the pre…