Katharina Eggensperger
University of Freiburg
32 Papers
72 Citations
Katharina Eggensperger is an academic researcher from University of Freiburg. The author has contributed to research in topics: Computer science & Hyperparameter optimization. The author has an hindex of 13, co-authored 26 publications.
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Papers
Deep learning with convolutional neural networks for EEG decoding and visualization.
Robin Tibor Schirrmeister,Jost Tobias Springenberg,Lukas D. J. Fiederer,Martin Glasstetter,Katharina Eggensperger,Michael Tangermann,Frank Hutter,Wolfram Burgard,Tonio Ball +8 more
TL;DR: This study shows how to design and train convolutional neural networks to decode task‐related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG‐based brain mapping.
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•Proceedings Article
Efficient and robust automated machine learning
Matthias Feurer,Aaron Klein,Katharina Eggensperger,Jost Tobias Springenberg,Manuel Blum,Frank Hutter +5 more
- 07 Dec 2015
TL;DR: This work introduces a robust new AutoML system based on scikit-learn, which improves on existing AutoML methods by automatically taking into account past performance on similar datasets, and by constructing ensembles from the models evaluated during the optimization.
Auto-sklearn: Efficient and Robust Automated Machine Learning
Matthias Feurer,Aaron Klein,Katharina Eggensperger,Jost Tobias Springenberg,Manuel Blum,Frank Hutter +5 more
- 01 Jan 2019
TL;DR: A robust new AutoML system based on the Python machine learning package scikit-learn, which improves on existing AutoML methods by automatically taking into account past performance on similar datasets, and by constructing ensembles from the models evaluated during the optimization.
Deep learning with convolutional neural networks for decoding and visualization of EEG pathology
Robin Tibor Schirrmeister,L. Gemein,Katharina Eggensperger,Frank Hutter,Tonio Ball +4 more
- 26 Aug 2017
TL;DR: The ConvNets and visualization techniques used in this study constitute a next step towards clinically useful automated EEG diagnosis and establish a new baseline for future work on this topic.
Proceedings Article
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
Noah Hollmann,Samuel Müller,Katharina Eggensperger,Frank Hutter +3 more
- 05 Jul 2022
TL;DR: TabPFN is a trained Transformer that can do supervised classi-cation for small tabular datasets in less than a second, needs no hyperparameter tuning and is competitive with state-of-the-art classification methods.