170 citations · 203 across the 21 of their papers we have counts for
7 papers · 1 filter
ResLRP: The Role of Residual Cancellation in Attribution Instability in Vision Transformers
Jim Berend, Reduan Achtibat, Daniel Schäffer +4
Vision Transformers (ViTs) are central to most modern vision models, yet obtaining input attributions that are fine-grained, faithful, and stable remains challenging. Layer-wise Re…
Safety Signals to Verify NetOps Agents with Action-Level Granularity
Tobias Labarta, Frederik Pahde, Novak Boškov +6
Agentic Network Operations (NetOps) are an emerging paradigm promising to enable workload-aware, self-adjustable, and reliable autonomous networks. While agents have proven their v…
ICON decomposition: Multivariate concept auditing reveals shortcuts that neural networks rely on
Roshan Prakash Rane, Marco Simnacher, Manuel Pfeuffer +7
Deep neural networks often exploit spurious associations, a failure known as shortcut learning. Before deployment, models should therefore be audited for reliance on concepts such…
Concept-based explanation of gene expression prediction from H&E images
Amos Muench, Jonathan Thielmann, Reduan Achtibat +9
Recent advances in pathology foundation models have enabled accurate prediction of spatial transcriptomics (ST) from routine H&E images. However, existing explainability methods fo…
From Attribution to Action: A Human-Centered Application of Activation Steering
Tobias Labarta, Maximilian Dreyer, Katharina Weitz +2
Explainable AI (XAI) methods reveal which features influence model predictions, yet provide limited means for practitioners to act on these explanations. Activation steering of com…
Contrastive Semantic Projection: Faithful Neuron Labeling with Contrastive Examples
Oussama Bouanani, Jim Berend, Wojciech Samek +2
Neuron labeling assigns textual descriptions to internal units of deep networks. Existing approaches typically rely on highly activating examples, often yielding broad or misleadin…
X-SYS: A Reference Architecture for Interactive Explanation Systems
Tobias Labarta, Nhi Hoang, Maximilian Dreyer +5
The explainable AI (XAI) research community has proposed numerous technical methods, yet deploying explainability as systems remains challenging: Interactive explanation systems re…