@inproceedings{BlaneckBornheimGriegeretal.2022,
  author    = {Blaneck, Patrick Gustav and Bornheim, Tobias and Grieger, Niklas and Bialonski, Stephan},
  title     = {Automatic readability assessment of german sentences with transformer ensembles},
  series = {Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text},
  booktitle = {Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text},
  publisher = {Association for Computational Linguistics},
  address   = {Potsdam},
  doi       = {10.48550/arXiv.2209.04299},
  pages     = {57 -- 62},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{SchwabedalSippelBrandtetal.2018,
  author    = {Schwabedal, Justus T. C. and Sippel, Daniel and Brandt, Moritz D. and Bialonski, Stephan},
  title     = {Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning},
  doi       = {10.48550/arXiv.1809.08443},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}
@article{BialonskiLehnertz2013,
  author    = {Bialonski, Stephan and Lehnertz, Klaus},
  title     = {Assortative mixing in functional brain networks during epileptic seizures},
  series = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
  volume    = {23},
  journal   = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
  number    = {3},
  doi       = {10.1063/1.4821915},
  pages     = {033139},
  year      = {2013},
  language  = {en}
}
@incollection{Bialonski2016,
  author    = {Bialonski, Stephan},
  title     = {Are interaction clusters in epileptic networks predictive of seizures?},
  series = {Epilepsy: The Intersection of Neurosciences, Biology, Mathematics, Engineering, and Physics},
  booktitle = {Epilepsy: The Intersection of Neurosciences, Biology, Mathematics, Engineering, and Physics},
  publisher = {CRC Press},
  isbn      = {978-143983886-0},
  pages     = {349 -- 355},
  year      = {2016},
  language  = {en}
}
@article{BialonskiAllefeldWellmeretal.2008,
  author    = {Bialonski, Stephan and Allefeld, C. and Wellmer, J. and Elger, C. and Lehnertz, K.},
  title     = {An approach to identify synchronization clusters within the epileptic network},
  series = {Klinische Neurophysiologie},
  volume    = {39},
  journal   = {Klinische Neurophysiologie},
  number    = {1},
  doi       = {10.1055/s-2008-1072881},
  pages     = {A79},
  year      = {2008},
  language  = {en}
}