70 citations · 130 across the 41 of their papers we have counts for
43 papers
Implementation Guidelines for Data Quality Metrics
Philipp Jung, Katinka Becker, Felix Biessmann +4
Despite decades of data quality (DQ) research, a gap remains between DQ dimensions, such as accuracy or completeness, which the literature defines in textual form, and DQ tools, wh…
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…
PolyChirp: Multi-Species Birdsong Classification Using TinyML on Low-Power Acoustic Sensors
Nathan Duboisset, Zhaolan Huang, Felix Bießmann +3
Recent progress in the field of TinyML has demonstrated that low-power hardware based on microcontrollers can achieve bird species monitoring in real time based on acoustic sensor…
Accuracy and Robustness of Model Cascades Under Data Perturbations
Pallavi Mitra, Jai Kushwaha, Felix Biessmann
Prediction cascades significantly reduce energy consumption of Artificial Intelligence (AI) models while maintaining high predictive performance. The idea is that easy inputs are r…
Evaluating Structured Information Extraction with Open Models in a High Risk Public Sector Application
Elias Schubert, Felix Bießmann
The extraction of structured information from unstructured documents represents a critical component of digital transformations in all sectors. While proprietary solutions dominate…
Global Crises and National Policies: A Large Scale Analysis of Political Content in German Language Online Media
Yara Döring, Felix Bießmann
Today most media content is consumed based on algorithmic recommendations. Evidence suggests that this can lead to politically biased media consumption patterns. Automated extracti…
Redakto - The Incognito Tab for LLMs
Saurav Kumar Saha, Tom Röhr, Felix Bießmann
Large Language Models (LLMs) are being increasingly used in everyday applications. A major challenge in the context of LLMs or Artificial Intelligence (AI) in general is to ensure…
CURED: Creating, Understanding, and Repairing Errors Demonstrator
Nicholas Chandler, Sebastian Jäger, Philipp Jung +1
Detecting and cleaning errors in tabular data is a prerequisite for data intense software applications. Recent research at the intersection of Machine Learning (ML) and Database Ma…
A Resilient Solution for Sewer Overflow Monitoring across Cloud and Edge
Vipin Singh, Tianheng Ling, Peter Ghaly +3
Aging combined sewer systems in many historical cities are increasingly stressed by extreme rainfall events, which can trigger combined sewer overflows (CSO) with significant envir…
RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy
Mario Koddenbrock, Christoph Lange, Robin Legner +6
Machine learning (ML) has transformed many scientific fields, yet key applications still lack standardized benchmarks. Raman spectroscopy, a widely used technique for non-invasive…
Monocular 3D Object Position Estimation with VLMs for Human-Robot Interaction
Ari Wahl, Dorian Gawlinski, David Przewozny +3
Pre-trained general-purpose Vision-Language Models (VLM) hold the potential to enhance intuitive human-machine interactions due to their rich world knowledge and 2D object detectio…
MechDetect: Detecting Data-Dependent Errors
Philipp Jung, Nicholas Chandler, Sebastian Jäger +1
Data quality monitoring is a core challenge in modern information processing systems. While many approaches to detect data errors or shifts have been proposed, few studies investig…
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…
Automated Energy-Aware Time-Series Model Deployment on Embedded FPGAs for Resilient Combined Sewer Overflow Management
Tianheng Ling, Vipin Singh, Chao Qian +2
Extreme weather events, intensified by climate change, increasingly challenge aging combined sewer systems, raising the risk of untreated wastewater overflow. Accurate forecasting…
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…
The Influence of Faulty Labels in Data Sets on Human Pose Estimation
Arnold Schwarz, Levente Hernadi, Felix Bießmann +1
In this study we provide empirical evidence demonstrating that the quality of training data impacts model performance in Human Pose Estimation (HPE). Inaccurate labels in widely us…
Generating Synthetic Satellite Imagery for Rare Objects: An Empirical Comparison of Models and Metrics
Tuong Vy Nguyen, Johannes Hoster, Alexander Glaser +2
Generative deep learning architectures can produce realistic, high-resolution fake imagery -- with potentially drastic societal implications. A key question in this context is: How…
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…
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…
Automated Computational Energy Minimization of ML Algorithms using Constrained Bayesian Optimization
Pallavi Mitra, Felix Biessmann
Bayesian optimization (BO) is an efficient framework for optimization of black-box objectives when function evaluations are costly and gradient information is not easily accessible…
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…