10 papers
Explaining Tabular Foundation Model Differences Through Meta-Features
Markus Herre, Andrej Tschalzev, Sascha Marton +1
With the rise of tabular foundation models alongside traditional models still performing well on many tasks, choosing the right model for a tabular dataset remains difficult. We in…
Concepts in Motion: Temporal Concept Bottleneck Model for Interpretable Video Classification
Patrick Knab, Sascha Marton, Philipp J. Schubert +2
Concept Bottleneck Models (CBMs) enable interpretable image classification by structuring predictions around human-understandable concepts, but extending this paradigm to video rem…
Learning Tree-Based Models with Gradient Descent
Sascha Marton
Tree-based models are widely recognized for their interpretability and have proven effective in various application domains, particularly in high-stakes domains. However, learning…
Beyond Pixels: Enhancing LIME with Hierarchical Features and Segmentation Foundation Models
Patrick Knab, Sascha Marton, Christian Bartelt
LIME (Local Interpretable Model-agnostic Explanations) is a popular XAI framework for unraveling decision-making processes in vision machine-learning models. The technique utilizes…
DCBM: Data-Efficient Visual Concept Bottleneck Models
Katharina Prasse, Patrick Knab, Sascha Marton +2
Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts. However, current CBMs typically rely on con…
Which LIME should I trust? Concepts, Challenges, and Solutions
Patrick Knab, Sascha Marton, Udo Schlegel +1
As neural networks become dominant in essential systems, Explainable Artificial Intelligence (XAI) plays a crucial role in fostering trust and detecting potential misbehavior of op…