TY  - CHAP
A1  - Blaneck, Patrick Gustav
A1  - Bornheim, Tobias
A1  - Grieger, Niklas
A1  - Bialonski, Stephan
T1  - Automatic readability assessment of german sentences with transformer ensembles
T2  - Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text
N2  - Reliable methods for automatic readability assessment have the potential to impact a variety of fields, ranging from machine translation to self-informed learning. Recently, large language models for the German language (such as GBERT and GPT-2-Wechsel) have become available, allowing to develop Deep Learning based approaches that promise to further improve automatic readability assessment. 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. We combined these models with linguistic features and investigated the dependence of prediction performance on ensemble size and composition. Mixed ensembles of GBERT and GPT-2-Wechsel performed better than ensembles of the same size consisting of only GBERT or GPT-2-Wechsel models. Our models were evaluated in the GermEval 2022 Shared Task on Text Complexity Assessment on data of German sentences. On out-of-sample data, our best ensemble achieved a root mean squared error of 0:435.
Y1  - 2022
U6  - http://dx.doi.org/10.48550/arXiv.2209.04299
N1  - Proceedings of the 18th Conference on Natural Language
Processing/Konferenz zur Verarbeitung natürlicher
Sprache (KONVENS 2022)
12-15 September, 2022
University of Potsdam
Potsdam, Germany
SP  - 57
EP  - 62
PB  - Association for Computational Linguistics
CY  - Potsdam
ER  - 
TY  - JOUR
A1  - Schwabedal, Justus T. C.
A1  - Sippel, Daniel
A1  - Brandt, Moritz D.
A1  - Bialonski, Stephan
T1  - Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning
N2  - Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle.
Y1  - 2018
U6  - http://dx.doi.org/10.48550/arXiv.1809.08443
ER  - 
TY  - JOUR
A1  - Bialonski, Stephan
A1  - Lehnertz, Klaus
T1  - Assortative mixing in functional brain networks during epileptic seizures
JF  - Chaos: An Interdisciplinary Journal of Nonlinear Science
Y1  - 2013
U6  - http://dx.doi.org/10.1063/1.4821915
VL  - 23
IS  - 3
SP  - 033139
ER  - 
TY  - CHAP
A1  - Bialonski, Stephan
T1  - Are interaction clusters in epileptic networks predictive of seizures?
T2  - Epilepsy: The Intersection of Neurosciences, Biology, Mathematics, Engineering, and Physics
Y1  - 2016
SN  - 978-143983886-0
SP  - 349
EP  - 355
PB  - CRC Press
ER  - 
TY  - JOUR
A1  - Bialonski, Stephan
A1  - Allefeld, C.
A1  - Wellmer, J.
A1  - Elger, C.
A1  - Lehnertz, K.
T1  - An approach to identify synchronization clusters within the epileptic network
JF  - Klinische Neurophysiologie
Y1  - 2008
U6  - http://dx.doi.org/10.1055/s-2008-1072881
VL  - 39
IS  - 1
SP  - A79
ER  -