3 citations · 5 across the 9 of their papers we have counts for
9 papers
AnySleep: a channel-agnostic deep learning system for high-resolution sleep staging in multi-center cohorts
Niklas Grieger, Jannik Raskob, Siamak Mehrkanoon +1
Sleep is essential for health, yet studying its dynamics requires manual sleep staging, a labor-intensive step in research and clinical care. Across centers, polysomnography (PSG)…
AIxcellent Vibes at GermEval 2025 Shared Task on Candy Speech Detection: Improving Model Performance by Span-Level Training
Christian Rene Thelen, Patrick Gustav Blaneck, Tobias Bornheim +2
Positive, supportive online communication in social media (candy speech) has the potential to foster civility, yet automated detection of such language remains underexplored, limit…
From Sleep Staging to Spindle Detection: A Case Study on End-to-End Automated Sleep Analysis
Niklas Grieger, Siamak Mehrkanoon, Philipp Ritter +1
Automation of sleep analysis, including both macrostructural (sleep stages) and microstructural (e.g., sleep spindles) elements, promises to enable large-scale sleep studies and to…
Detecting Sexism in German Online Newspaper Comments with Open-Source Text Embeddings (Team GDA, GermEval2024 Shared Task 1: GerMS-Detect, Subtasks 1 and 2, Closed Track)
Florian Bremm, Patrick Gustav Blaneck, Tobias Bornheim +2
Sexism in online media comments is a pervasive challenge that often manifests subtly, complicating moderation efforts as interpretations of what constitutes sexism can vary among i…
Data-Efficient Sleep Staging with Synthetic Time Series Pretraining
Niklas Grieger, Siamak Mehrkanoon, Stephan Bialonski
Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small data…
Speaker attribution in German parliamentary debates with QLoRA-adapted large language models
Tobias Bornheim, Niklas Grieger, Patrick Gustav Blaneck +1
The growing body of political texts opens up new opportunities for rich insights into political dynamics and ideologies but also increases the workload for manual analysis. Automat…
ur-iw-hnt at GermEval 2021: An Ensembling Strategy with Multiple BERT Models
Hoai Nam Tran, Udo Kruschwitz
This paper describes our approach (ur-iw-hnt) for the Shared Task of GermEval2021 to identify toxic, engaging, and fact-claiming comments. We submitted three runs using an ensembli…
FHAC at GermEval 2021: Identifying German toxic, engaging, and fact-claiming comments with ensemble learning
Tobias Bornheim, Niklas Grieger, Stephan Bialonski
The availability of language representations learned by large pretrained neural network models (such as BERT and ELECTRA) has led to improvements in many downstream Natural Languag…
FH-SWF SG at GermEval 2021: Using Transformer-Based Language Models to Identify Toxic, Engaging, & Fact-Claiming Comments
Christian Gawron, Sebastian Schmidt
In this paper we describe the methods we used for our submissions to the GermEval 2021 shared task on the identification of toxic, engaging, and fact-claiming comments. For all thr…