Dawid Damian Rymarczyk — accepted AI-conference papers

2025

LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision

Mateusz Pach, Koryna Lewandowska, Jacek Tabor, Bartosz Michał Zieliński, Dawid Damian Rymarczyk

ICLR 2025poster

Prototypical parts networks combine the power of deep learning with the explainability of case-based reasoning to make accurate, interpretable decisions. They follow the this looks like that reasoning, representing each prototypical part with patches from training images. However, a single image pat…

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2025

SEMU: Singular Value Decomposition for Efficient Machine Unlearning

Marcin Sendera, Łukasz Struski, Kamil Książek, Kryspin Musiol, Jacek Tabor, Dawid Damian Rymarczyk

ICML 2025poster

While the capabilities of generative foundational models have advanced rapidly in recent years, methods to prevent harmful and unsafe behaviors remain underdeveloped. Among the pressing challenges in AI safety, machine unlearning (MU) has become increasingly critical to meet upcoming safety regulati…

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