105 citations · 441 across the 38 of their papers we have counts for
42 papers
MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysis
Angelo Yamachui Sitcheu, Nils Friederich, Simon Baeuerle +3
Nowadays, Machine Learning (ML) is experiencing tremendous popularity that has never been seen before. The operationalization of ML models is governed by a set of concepts and meth…
Context-Aware Composition of Agent Policies by Markov Decision Process Entity Embeddings and Agent Ensembles
Nicole Merkle, Ralf Mikut
Computational agents support humans in many areas of life and are therefore found in heterogeneous contexts. This means they operate in rapidly changing environments and can be con…
Transformer Training Strategies for Forecasting Multiple Load Time Series
Matthias Hertel, Maximilian Beichter, Benedikt Heidrich +4
In the smart grid of the future, accurate load forecasts on the level of individual clients can help to balance supply and demand locally and to prevent grid outages. While the num…
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Simon Graham, Quoc Dang Vu, Mostafa Jahanifar +86
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovatio…
ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information
Benedikt Heidrich, Kaleb Phipps, Oliver Neumann +3
Probabilistic forecasts are essential for various downstream applications such as business development, traffic planning, and electrical grid balancing. Many of these probabilistic…
Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks
Kaleb Phipps, Benedikt Heidrich, Marian Turowski +3
In various applications, probabilistic forecasts are required to quantify the inherent uncertainty associated with the forecast. However, numerous modern forecasting methods are st…
AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models
Stefan Meisenbacher, Benedikt Heidrich, Tim Martin +2
Accurate PhotoVoltaic (PV) power generation forecasting is vital for the efficient operation of Smart Grids. The automated design of such accurate forecasting models for individual…
EasyMLServe: Easy Deployment of REST Machine Learning Services
Oliver Neumann, Marcel Schilling, Markus Reischl +1
Various research domains use machine learning approaches because they can solve complex tasks by learning from data. Deploying machine learning models, however, is not trivial and…
Predicting the power grid frequency of European islands
Thorbjørn Lund Onsaker, Heidi S. Nygård, Damià Gomila +7
Modelling, forecasting and overall understanding of the dynamics of the power grid and its frequency are essential for the safe operation of existing and future power grids. Much p…
Rapid Flow Behavior Modeling of Thermal Interface Materials Using Deep Neural Networks
Simon Baeuerle, Marius Gebhardt, Jonas Barth +2
Thermal Interface Materials (TIMs) are widely used in electronic packaging. Increasing power density and limited assembly space pose high demands on thermal management. Large cooli…
EmbedTrack -- Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths
Katharina Löffler, Ralf Mikut
A systematic analysis of the cell behavior requires automated approaches for cell segmentation and tracking. While deep learning has been successfully applied for the task of cell…
Exploiting Multiple EEG Data Domains with Adversarial Learning
David Bethge, Philipp Hallgarten, Ozan Özdenizci +3
Electroencephalography (EEG) is shown to be a valuable data source for evaluating subjects' mental states. However, the interpretation of multi-modal EEG signals is challenging, as…
ciscNet -- A Single-Branch Cell Instance Segmentation and Classification Network
Moritz Böhland, Oliver Neumann, Marcel P. Schilling +4
Automated cell nucleus segmentation and classification are required to assist pathologists in their decision making. The Colon Nuclei Identification and Counting Challenge 2022 (Co…
Review of automated time series forecasting pipelines
Stefan Meisenbacher, Marian Turowski, Kaleb Phipps +4
Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case require…
Domain-Invariant Representation Learning from EEG with Private Encoders
David Bethge, Philipp Hallgarten, Tobias Grosse-Puppendahl +4
Deep learning based electroencephalography (EEG) signal processing methods are known to suffer from poor test-time generalization due to the changes in data distribution. This beco…
Label Assistant: A Workflow for Assisted Data Annotation in Image Segmentation Tasks
Marcel P. Schilling, Luca Rettenberger, Friedrich Münke +5
Recent research in the field of computer vision strongly focuses on deep learning architectures to tackle image processing problems. Deep neural networks are often considered in co…
Smart Data Representations: Impact on the Accuracy of Deep Neural Networks
Oliver Neumann, Nicole Ludwig, Marian Turowski +3
Deep Neural Networks are able to solve many complex tasks with less engineering effort and better performance. However, these networks often use data for training and evaluation wi…
Concepts for Automated Machine Learning in Smart Grid Applications
Stefan Meisenbacher, Janik Pinter, Tim Martin +2
Undoubtedly, the increase of available data and competitive machine learning algorithms has boosted the popularity of data-driven modeling in energy systems. Applications are forec…
pyWATTS: Python Workflow Automation Tool for Time Series
Benedikt Heidrich, Andreas Bartschat, Marian Turowski +7
Time series data are fundamental for a variety of applications, ranging from financial markets to energy systems. Due to their importance, the number and complexity of tools and me…
Probabilistic Solar Power Forecasting: Long Short-Term Memory Network vs Simpler Approaches
Vinayak Sharma, Jorge Angel Gonzalez Ordiano, Ralf Mikut +1
The high penetration of volatile renewable energy sources such as solar make methods for coping with the uncertainty associated with them of paramount importance. Probabilistic for…
Data-Driven Copy-Paste Imputation for Energy Time Series
Moritz Weber, Marian Turowski, Hüseyin K. Çakmak +3
A cornerstone of the worldwide transition to smart grids are smart meters. Smart meters typically collect and provide energy time series that are vital for various applications, su…
CAD2Real: Deep learning with domain randomization of CAD data for 3D pose estimation of electronic control unit housings
Simon Baeuerle, Jonas Barth, Elton Renato Tavares de Menezes +2
Electronic control units (ECUs) are essential for many automobile components, e.g. engine, anti-lock braking system (ABS), steering and airbags. For some products, the 3D pose of e…
Evaluating Ensemble Post-Processing for Wind Power Forecasts
Kaleb Phipps, Sebastian Lerch, Maria Andersson +3
Capturing the uncertainty in probabilistic wind power forecasts is challenging, especially when uncertain input variables, such as the weather, play a role. Since ensemble weather…
Integrating Battery Aging in the Optimization for Bidirectional Charging of Electric Vehicles
Karl Schwenk, Stefan Meisenbacher, Benjamin Briegel +3
Smart charging of Electric Vehicles (EVs) reduces operating costs, allows more sustainable battery usage, and promotes the rise of electric mobility. In addition, bidirectional cha…