Zobrazeno 1 - 10
of 4 314
pro vyhledávání: '"Steinert, P."'
Autor:
Scholz, Stefan, Weidmann, Nils B., Steinert-Threlkeld, Zachary C., Keremoğlu, Eda, Goldlücke, Bastian
Treating images as data has become increasingly popular in political science. While existing classifiers for images reach high levels of accuracy, it is difficult to systematically assess the visual features on which they base their classification. T
Externí odkaz:
http://arxiv.org/abs/2407.03786
Autor:
Bley, Jonas, Rexigel, Eva, Arias, Alda, Krupp, Lars, Steinert, Steffen, Longen, Nikolas, Lukowicz, Paul, Küchemann, Stefan, Kuhn, Jochen, Kiefer-Emmanouilidis, Maximilian, Widera, Artur
In the rapidly evolving interdisciplinary field of quantum information science and technology, a big obstacle is the necessity of understanding high-level mathematics to solve complex problems. Visualizations like the (dimensional) circle notation en
Externí odkaz:
http://arxiv.org/abs/2406.16556
Autor:
Wolff, Miriam K., Royston, Sam, Fougner, Anders Lyngvi, Schaathun, Hans Georg, Steinert, Martin, Volden, Rune
Blood glucose prediction is an important component of biomedical technology for managing diabetes with automated insulin delivery systems. Machine learning and deep learning algorithms hold the potential to advance this technology. However, the lack
Externí odkaz:
http://arxiv.org/abs/2406.08915
Autor:
Patil, Abhinav, Jumelet, Jaap, Chiu, Yu Ying, Lapastora, Andy, Shen, Peter, Wang, Lexie, Willrich, Clevis, Steinert-Threlkeld, Shane
This paper introduces Filtered Corpus Training, a method that trains language models (LMs) on corpora with certain linguistic constructions filtered out from the training data, and uses it to measure the ability of LMs to perform linguistic generaliz
Externí odkaz:
http://arxiv.org/abs/2405.15750
The "massively-multilingual" training of multilingual models is known to limit their utility in any one language, and they perform particularly poorly on low-resource languages. However, there is evidence that low-resource languages can benefit from
Externí odkaz:
http://arxiv.org/abs/2405.12413
Autor:
Ege, Daniel Nygård, Øvrebø, Henrik H., Stubberud, Vegar, Berg, Martin Francis, Elverum, Christer, Steinert, Martin, Vestad, Håvard
This study compares the design practices and performance of ChatGPT 4.0, a large language model (LLM), against graduate engineering students in a 48-hour prototyping hackathon, based on a dataset comprising more than 100 prototypes. The LLM participa
Externí odkaz:
http://arxiv.org/abs/2404.18479
Cerulli Irelli and Lanini have shown that PBW degenerations of flag varieties in type A and C are actually Schubert varieties of higher rank. We introduce Dynkin cones to parameterise specific abelianisations of classical Lie algebras. Within this fr
Externí odkaz:
http://arxiv.org/abs/2404.05277
Unsupervised on-the-fly back-translation, in conjunction with multilingual pretraining, is the dominant method for unsupervised neural machine translation. Theoretically, however, the method should not work in general. We therefore conduct controlled
Externí odkaz:
http://arxiv.org/abs/2403.18031
Technology-facilitated gender-based violence has become a global threat to women's political representation and democracy. Understanding how online hate affects its targets is thus paramount. We analyse 10 million tweets directed at female candidates
Externí odkaz:
http://arxiv.org/abs/2403.07523
SCANIA Component X Dataset: A Real-World Multivariate Time Series Dataset for Predictive Maintenance
This paper presents a description of a real-world, multivariate time series dataset collected from an anonymized engine component (called Component X) of a fleet of trucks from SCANIA, Sweden. This dataset includes diverse variables capturing detaile
Externí odkaz:
http://arxiv.org/abs/2401.15199