Zobrazeno 1 - 10
of 111
pro vyhledávání: '"Bialonski, Stephan"'
Autor:
Bremm, Florian, Blaneck, Patrick Gustav, Bornheim, Tobias, Grieger, Niklas, Bialonski, Stephan
Publikováno v:
In Proc. GermEval 2024 Task 1 GerMS-Detect Workshop on Sexism Detection in German Online News Fora (GerMS-Detect 2024): 20th KONVENS 2024, pages 33-38. Online (2024)
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 individuals. We study monolingual and multilingual open-source text emb
Externí odkaz:
http://arxiv.org/abs/2409.10341
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. To address these challenges, various strategies, such as self-su
Externí odkaz:
http://arxiv.org/abs/2403.08592
Publikováno v:
Journal for Language Technology and Computational Linguistics, 37, 1-13 (2024)
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. Automated speaker attribution, which detects who said what to whom in a speec
Externí odkaz:
http://arxiv.org/abs/2309.09902
Publikováno v:
In Proc. GermEval 2022 Workshop on Text Complexity Assessment of German Text: 18th KONVENS 2022, pages 57-62, Online (2022)
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-Wechs
Externí odkaz:
http://arxiv.org/abs/2209.04299
Autor:
Kaulen, Lars, Schwabedal, Justus T. C., Schneider, Jules, Ritter, Philipp, Bialonski, Stephan
Publikováno v:
Scientific Reports 12, 7686 (2022)
Sleep spindles are neurophysiological phenomena that appear to be linked to memory formation and other functions of the central nervous system, and that can be observed in electroencephalographic recordings (EEG) during sleep. Manually identified spi
Externí odkaz:
http://arxiv.org/abs/2202.05158
Publikováno v:
In Proc. GermEval 2021 Workshop on Identification of Toxic, Engaging, and Fact-Claiming Comments: 17th KONVENS 2021, pages 105-111, Online (2021)
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. Pretrained models usually differ i
Externí odkaz:
http://arxiv.org/abs/2109.03094
Autor:
Grieger, Niklas, Schwabedal, Justus T. C., Wendel, Stefanie, Ritze, Yvonne, Bialonski, Stephan
Publikováno v:
S. Scientific Reports 11, 12245 (2021)
Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accurac
Externí odkaz:
http://arxiv.org/abs/2105.01933
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 field. We intr
Externí odkaz:
http://arxiv.org/abs/1809.08443
Publikováno v:
Phys. Rev. E 96, 042211 (2017)
The ability to reliably predict critical transitions in dynamical systems is a long-standing goal of diverse scientific communities. Previous work focused on early warning signals related to local bifurcations (critical slowing down) and non-bifurcat
Externí odkaz:
http://arxiv.org/abs/1711.04594
Autor:
Ngamga, Eulalie Joelle, Bialonski, Stephan, Marwan, Norbert, Kurths, Jürgen, Geier, Christian, Lehnertz, Klaus
We investigate the suitability of selected measures of complexity based on recurrence quantification analysis and recurrence networks for an identification of pre-seizure states in multi-day, multi-channel, invasive electroencephalographic recordings
Externí odkaz:
http://arxiv.org/abs/1602.07974