no code implementations • 17 May 2024 • Markus Bayer, Christian Reuter
In this way, ActiveLLM can even help other active learning strategies to overcome their cold start problem.
no code implementations • 24 Apr 2023 • Philipp Kuehn, Mike Schmidt, Markus Bayer, Christian Reuter
However, while this already solves the problem of extracting the information out of documents, the search for these documents is rarely considered.
no code implementations • 6 Dec 2022 • Markus Bayer, Philipp Kuehn, Ramin Shanehsaz, Christian Reuter
As this cannot be addressed manually, cybersecurity experts need to rely on machine learning techniques.
no code implementations • 5 Oct 2022 • Philipp Kuehn, David N. Relke, Christian Reuter
With this work, the publicly available web pages referenced in the National Vulnerability Database are analyzed and made available as sources of texts through web scraping.
no code implementations • 22 Jul 2022 • Markus Bayer, Tobias Frey, Christian Reuter
Since this requires a lot of labelled data using standard training methods, we combine three different low-data regime techniques - transfer learning, data augmentation, and few-shot learning - to train a high-quality classifier from very few labelled instances.
no code implementations • 7 Jul 2021 • Markus Bayer, Marc-André Kaufhold, Christian Reuter
Data augmentation, the artificial creation of training data for machine learning by transformations, is a widely studied research field across machine learning disciplines.
no code implementations • 26 Mar 2021 • Markus Bayer, Marc-André Kaufhold, Björn Buchhold, Marcel Keller, Jörg Dallmeyer, Christian Reuter
Thus, data augmentation methods have been developed to improve classifiers by artificially created training data.