6 citations · 15 across the 12 of their papers we have counts for
12 papers
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…
TinyChirp: Bird Song Recognition Using TinyML Models on Low-power Wireless Acoustic Sensors
Zhaolan Huang, Adrien Tousnakhoff, Polina Kozyr +5
Monitoring biodiversity at scale is challenging. Detecting and identifying species in fine grained taxonomies requires highly accurate machine learning (ML) methods. Training such…
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…
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…
Generating Synthetic Satellite Imagery With Deep-Learning Text-to-Image Models -- Technical Challenges and Implications for Monitoring and Verification
Tuong Vy Nguyen, Alexander Glaser, Felix Biessmann
Novel deep-learning (DL) architectures have reached a level where they can generate digital media, including photorealistic images, that are difficult to distinguish from real data…
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…