no code implementations • 15 Dec 2023 • Jens Müller, Lars Kühmichel, Martin Rohbeck, Stefan T. Radev, Ullrich Köthe
In this work, we analyze the conditions under which information about the context of an input $X$ can improve the predictions of deep learning models in new domains.
1 code implementation • 23 Jun 2023 • Felix Draxler, Lars Kühmichel, Armand Rousselot, Jens Müller, Christoph Schnörr, Ullrich Köthe
Gaussianization is a simple generative model that can be trained without backpropagation.
1 code implementation • 17 Mar 2023 • Jens Müller, Stefan T. Radev, Robert Schmier, Felix Draxler, Carsten Rother, Ullrich Köthe
We investigate a "learning to reject" framework to address the problem of silent failures in Domain Generalization (DG), where the test distribution differs from the training distribution.
no code implementations • 11 Jan 2022 • Hongliu Yang, Matthias Eberlein, Jens Müller, Ronald Tetzlaff
Repeated epileptic seizures impair around 65 million people worldwide and a successful prediction of seizures could significantly help patients suffering from refractory epilepsy.
3 code implementations • NeurIPS 2021 • Axel Sauer, Kashyap Chitta, Jens Müller, Andreas Geiger
Generative Adversarial Networks (GANs) produce high-quality images but are challenging to train.
Ranked #1 on Image Generation on Stanford Cars
no code implementations • 26 Oct 2021 • Jens Müller, Hongliu Yang, Matthias Eberlein, Georg Leonhardt, Ortrud Uckermann, Levin Kuhlmann, Ronald Tetzlaff
Here, we examine false and missing alarms of two algorithms on long-term datasets to show that the limitations are less related to classifiers or features, but rather to intrinsic changes in the data.
no code implementations • 14 Oct 2020 • Jens Müller, Robert Schmier, Lynton Ardizzone, Carsten Rother, Ullrich Köthe
Standard supervised learning breaks down under data distribution shift.
no code implementations • 2 Nov 2018 • Matthias Eberlein, Raphael Hildebrand, Ronald Tetzlaff, Nico Hoffmann, Levin Kuhlmann, Benjamin Brinkmann, Jens Müller
In this contribution, we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction.