5 code implementations • 15 Mar 2017 • Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, Tonio Ball
PLEASE READ AND CITE THE REVISED VERSION at Human Brain Mapping: http://onlinelibrary. wiley. com/doi/10. 1002/hbm. 23730/full Code available here: https://github. com/robintibor/braindecode
no code implementations • 20 Jul 2017 • Felix Burget, Lukas Dominique Josef Fiederer, Daniel Kuhner, Martin Völker, Johannes Aldinger, Robin Tibor Schirrmeister, Chau Do, Joschka Boedecker, Bernhard Nebel, Tonio Ball, Wolfram Burgard
As our results demonstrate, our system is capable of adapting to frequent changes in the environment and reliably completing given tasks within a reasonable amount of time.
no code implementations • 4 Aug 2017 • Dominik Welke, Joos Behncke, Marina Hader, Robin Tibor Schirrmeister, Andreas Schönau, Boris Eßmann, Oliver Müller, Wolfram Burgard, Tonio Ball
Our findings suggest that non-invasive recordings of brain responses elicited when observing robots indeed contain decodable information about the correctness of the robot's action and the type of observed robot.
2 code implementations • 26 Aug 2017 • Robin Tibor Schirrmeister, Lukas Gemein, Katharina Eggensperger, Frank Hutter, Tonio Ball
We apply convolutional neural networks (ConvNets) to the task of distinguishing pathological from normal EEG recordings in the Temple University Hospital EEG Abnormal Corpus.
no code implementations • 16 Nov 2017 • Joos Behncke, Robin Tibor Schirrmeister, Wolfram Burgard, Tonio Ball
Analysis of brain signals from a human interacting with a robot may help identifying robot errors, but accuracies of such analyses have still substantial space for improvement.
no code implementations • 21 Nov 2017 • Kay Gregor Hartmann, Robin Tibor Schirrmeister, Tonio Ball
Our findings thus provide insights into how ConvNets hierarchically represent spectral EEG features in their intermediate layers and suggest that ConvNets can exploit and might help to better understand the compositional structure of EEG time series.
2 code implementations • 5 Jun 2018 • Kay Gregor Hartmann, Robin Tibor Schirrmeister, Tonio Ball
Generative adversarial networks (GANs) are recently highly successful in generative applications involving images and start being applied to time series data.
1 code implementation • 5 Jun 2018 • Robin Tibor Schirrmeister, Patryk Chrabąszcz, Frank Hutter, Tonio Ball
This first attempt to use RevNets inside the adversarial autoencoder framework slightly underperformed relative to recent advanced generative models using an autoencoder component on CelebA, but this gap may diminish with further optimization of the training setup of generative RevNets.
no code implementations • 17 Jul 2019 • Robin Tibor Schirrmeister, Tonio Ball
In this manuscript, we investigate deep invertible networks for EEG-based brain signal decoding and find them to generate realistic EEG signals as well as classify novel signals above chance.
1 code implementation • 11 Feb 2020 • Lukas Alexander Wilhelm Gemein, Robin Tibor Schirrmeister, Patryk Chrabąszcz, Daniel Wilson, Joschka Boedecker, Andreas Schulze-Bonhage, Frank Hutter, Tonio Ball
The results demonstrate that the proposed feature-based decoding framework can achieve accuracies on the same level as state-of-the-art deep neural networks.
1 code implementation • NeurIPS 2020 • Robin Tibor Schirrmeister, Yuxuan Zhou, Tonio Ball, Dan Zhang
We refine previous investigations of this failure at anomaly detection for invertible generative networks and provide a clear explanation of it as a combination of model bias and domain prior: Convolutional networks learn similar low-level feature distributions when trained on any natural image dataset and these low-level features dominate the likelihood.
no code implementations • 14 Jan 2022 • Robin Tibor Schirrmeister, Rosanne Liu, Sara Hooker, Tonio Ball
To answer these questions, we need a clear measure of input simplicity (or inversely, complexity), an optimization objective that correlates with simplification, and a framework to incorporate such objective into training and inference.
no code implementations • 16 Jul 2022 • Diane Wagner, Fabio Ferreira, Danny Stoll, Robin Tibor Schirrmeister, Samuel Müller, Frank Hutter
Self-Supervised Learning (SSL) has become a very active area of Deep Learning research where it is heavily used as a pre-training method for classification and other tasks.
no code implementations • 20 Dec 2022 • Daniel Wilson, Robin Tibor Schirrmeister, Lukas Alexander Wilhelm Gemein, Tonio Ball
Our study aims to lay the groundwork in the area of these topics through the analysis of DRNs for EEG with a wide range of hyperparameters.
2 code implementations • 17 Feb 2024 • Benjamin Feuer, Robin Tibor Schirrmeister, Valeriia Cherepanova, Chinmay Hegde, Frank Hutter, Micah Goldblum, Niv Cohen, Colin White
Similar to large language models, PFNs make use of pretraining and in-context learning to achieve strong performance on new tasks in a single forward pass.