1 citations · 1 across the 8 of their papers we have counts for
9 papers
DAVE: Distribution-aware Attribution via ViT Gradient Decomposition
Adam Wróbel, Siddhartha Gairola, Jacek Tabor +3
Vision Transformers (ViTs) have become a dominant architecture in computer vision, yet producing stable and high-resolution attribution maps for these models remains challenging. A…
ProtoQuant: Quantization of Prototypical Parts For General and Fine-Grained Image Classification
Mikołaj Janusz, Adam Wróbel, Bartosz Zieliński +1
Prototypical parts-based models offer a "this looks like that" paradigm for intrinsic interpretability, yet they typically struggle with ImageNet-scale generalization and often req…
SpaRRTa: A Synthetic Benchmark for Evaluating Spatial Intelligence in Visual Foundation Models
Turhan Can Kargin, Wojciech Jasiński, Adam Pardyl +2
Visual Foundation Models (VFMs), such as DINO and CLIP, excel in semantic understanding of images but exhibit limited spatial reasoning capabilities, which limits their applicabili…
Milnor meets Hopf and Toeplitz at the K-theory of quantum projective planes
Francesco D'Andrea, Piotr M. Hajac, Tomasz Maszczyk +1
We explore applications of the celebrated construction of the Milnor connecting homomorphism from the odd to the even K-groups in the context of Hopf--Galois theory. For a finitely…
Enhancing Chemical Explainability Through Counterfactual Masking
Łukasz Janisiów, Marek Kochańczyk, Bartosz Zieliński +1
Molecular property prediction is a crucial task that guides the design of new compounds, including drugs and materials. While explainable artificial intelligence methods aim to scr…
Fragment-Wise Interpretability in Graph Neural Networks via Molecule Decomposition and Contribution Analysis
Sebastian Musiał, Bartosz Zieliński, Tomasz Danel
Graph neural networks have demonstrated remarkable success in predicting molecular properties by leveraging the rich structural information encoded in molecular graphs. However, th…