Search Results for author:
Found 7 papers, 5 papers with code
Data-Efficient Sleep Staging with Synthetic Time Series Pretraining
Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets.
Speaker attribution in German parliamentary debates with QLoRA-adapted large language models
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.
Automatic Readability Assessment of German Sentences with Transformer Ensembles
In this contribution, we studied the ability of ensembles of fine-tuned GBERT and GPT-2-Wechsel models to reliably predict the readability of German sentences.
Lexical Complexity Prediction
Text Complexity Assessment (GermEval 2022)
Advanced sleep spindle identification with neural networks
Our model's performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset.
Ranked #1 on
Spindle Detection
on MODA dataset
FHAC at GermEval 2021: Identifying German toxic, engaging, and fact-claiming comments with ensemble learning
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 Language Processing tasks in recent years.
Classification of toxic, engaging, fact-claiming comments
Engaging Comment Classification
+3
Automated scoring of pre-REM sleep in mice with deep learning
When restricted to the classical stages, the optimized network showed state-of-the-art classification performance with an out-of-sample F1 score of 0. 95 in male C57BL/6J mice.
Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning
Sleep scoring is a necessary and time-consuming task in sleep studies.
