3 citations · 11 across the 12 of their papers we have counts for
13 papers
Verifying the Linear Representation Hypothesis: How Interpretable Are Vision SAEs?
Teodor Chiaburu, Franz Motzkus, Frank Haußer +1
Vision Sparse Autoencoders (SAEs) have become a popular tool in Mechanistic Interpretability due to their presumed ability to disentangle complex features learned by a model into m…
No Single Metric Tells the Whole Story: A Multi-Dimensional Evaluation Framework for Uncertainty Attributions
Emily Schiller, Teodor Chiaburu, Marco Zullich +1
Research on explainable AI (XAI) has frequently focused on explaining model predictions. More recently, methods have been proposed to explain prediction uncertainty by attributing…
Uncertainty-Guided Expert-AI Collaboration for Efficient Soil Horizon Annotation
Teodor Chiaburu, Vipin Singh, Frank Haußer +1
Uncertainty quantification is essential in human-machine collaboration, as human agents tend to adjust their decisions based on the confidence of the machine counterpart. Reliably…
SoilNet: A Multimodal Multitask Model for Hierarchical Classification of Soil Horizons
Vipin Singh, Teodor Chiaburu, Einar Eberhardt +4
Recent advances in artificial intelligence (AI), in particular foundation models, have improved the state of the art in many application domains including geosciences. Some specifi…
Evaluating Time Series Models for Urban Wastewater Management: Predictive Performance, Model Complexity and Resilience
Vipin Singh, Tianheng Ling, Teodor Chiaburu +1
Climate change increases the frequency of extreme rainfall, placing a significant strain on urban infrastructures, especially Combined Sewer Systems (CSS). Overflows from overburde…
Uncertainty Propagation in XAI: A Comparison of Analytical and Empirical Estimators
Teodor Chiaburu, Felix Bießmann, Frank Haußer
Understanding uncertainty in Explainable AI (XAI) is crucial for building trust and ensuring reliable decision-making in Machine Learning models. This paper introduces a unified fr…
Confident Teacher, Confident Student? A Novel User Study Design for Investigating the Didactic Potential of Explanations and their Impact on Uncertainty
Teodor Chiaburu, Frank Haußer, Felix Bießmann
Evaluating the quality of explanations in Explainable Artificial Intelligence (XAI) is to this day a challenging problem, with ongoing debate in the research community. While some…
Data-driven Modeling of Combined Sewer Systems for Urban Sustainability: An Empirical Evaluation
Vipin Singh, Tianheng Ling, Teodor Chiaburu +1
Climate change poses complex challenges, with extreme weather events becoming increasingly frequent and difficult to model. Examples include the dynamics of Combined Sewer Systems…
Multisensor Data Fusion for Automatized Insect Monitoring (KInsecta)
Martin Tschaikner, Danja Brandt, Henning Schmidt +7
Insect populations are declining globally, making systematic monitoring essential for conservation. Most classical methods involve death traps and counter insect conservation. This…
Low Cost Machine Vision for Insect Classification
Danja Brandt, Martin Tschaikner, Teodor Chiaburu +5
Preserving the number and diversity of insects is one of our society's most important goals in the area of environmental sustainability. A prerequisite for this is a systematic and…
CoProNN: Concept-based Prototypical Nearest Neighbors for Explaining Vision Models
Teodor Chiaburu, Frank Haußer, Felix Bießmann
Mounting evidence in explainability for artificial intelligence (XAI) research suggests that good explanations should be tailored to individual tasks and should relate to concepts…
Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows
Teodor Chiaburu, Felix Biessmann
Climate change poses increasingly complex challenges to our society. Extreme weather events such as floods, wild fires or droughts are becoming more frequent, spontaneous and diffi…
Towards ML Methods for Biodiversity: A Novel Wild Bee Dataset and Evaluations of XAI Methods for ML-Assisted Rare Species Annotations
Teodor Chiaburu, Felix Biessmann, Frank Hausser
Insects are a crucial part of our ecosystem. Sadly, in the past few decades, their numbers have worryingly decreased. In an attempt to gain a better understanding of this process a…