70 citations · 130 across the 41 of their papers we have counts for
19 papers · 1 filter
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…
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…
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…
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…
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…
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…
GreenDB -- A Dataset and Benchmark for Extraction of Sustainability Information of Consumer Goods
Sebastian Jäger, Alexander Flick, Jessica Adriana Sanchez Garcia +3
The production, shipping, usage, and disposal of consumer goods have a substantial impact on greenhouse gas emissions and the depletion of resources. Machine Learning (ML) can help…
GreenDB: Toward a Product-by-Product Sustainability Database
Sebastian Jäger, Jessica Greene, Max Jakob +3
The production, shipping, usage, and disposal of consumer goods have a substantial impact on greenhouse gas emissions and the depletion of resources. Modern retail platforms rely h…
CycleSense: Detecting Near Miss Incidents in Bicycle Traffic from Mobile Motion Sensors
Ahmet-Serdar Karakaya, Thomas Ritter, Felix Biessmann +1
In cities worldwide, cars cause health and traffic problems whichcould be partly mitigated through an increased modal share of bicycles. Many people, however, avoid cycling due to…
Quantifying Interpretability and Trust in Machine Learning Systems
Philipp Schmidt, Felix Biessmann
Decisions by Machine Learning (ML) models have become ubiquitous. Trusting these decisions requires understanding how algorithms take them. Hence interpretability methods for ML ar…
Doubly stochastic large scale kernel learning with the empirical kernel map
Nikolaas Steenbergen, Sebastian Schelter, Felix Bießmann
With the rise of big data sets, the popularity of kernel methods declined and neural networks took over again. The main problem with kernel methods is that the kernel matrix grows…
Canonical Trends: Detecting Trend Setters in Web Data
Felix Biessmann, Jens-Michalis Papaioannou, Mikio Braun +1
Much information available on the web is copied, reused or rephrased. The phenomenon that multiple web sources pick up certain information is often called trend. A central problem…