2 citations · 3 across the 7 of their papers we have counts for
9 papers
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…
Playing the network backward: A Game Theoretic Attribution Framework
Jakob Paul Zimmermann, Jim Berend, Georg Loho +2
Attribution methods explain which input features drive a model's prediction, making them central to model debugging and mechanistic interpretability. Yet backward attribution metho…
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…
Atlas-Alignment: Making Interpretability Transferable Across Language Models
Bruno Puri, Jim Berend, Sebastian Lapuschkin +1
Interpretability is crucial for building safe, reliable, and controllable language models, yet existing interpretability pipelines remain costly and difficult to scale. Interpretin…
From What to How: Attributing CLIP's Latent Components Reveals Unexpected Semantic Reliance
Maximilian Dreyer, Lorenz Hufe, Jim Berend +3
Transformer-based CLIP models are widely used for text-image probing and feature extraction, making it relevant to understand the internal mechanisms behind their predictions. Whil…
Mechanistic understanding and validation of large AI models with SemanticLens
Maximilian Dreyer, Jim Berend, Tobias Labarta +4
Unlike human-engineered systems such as aeroplanes, where each component's role and dependencies are well understood, the inner workings of AI models remain largely opaque, hinderi…
Beyond Scalars: Concept-Based Alignment Analysis in Vision Transformers
Johanna Vielhaben, Dilyara Bareeva, Jim Berend +2
Vision transformers (ViTs) can be trained using various learning paradigms, from fully supervised to self-supervised. Diverse training protocols often result in significantly diffe…
Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation
Leander Weber, Jim Berend, Moritz Weckbecker +4
Gradient-based optimization has been a cornerstone of machine learning that enabled the vast advances of Artificial Intelligence (AI) development over the past decades. However, th…