Search Results for author: Robin Tibor Schirrmeister

Found 13 papers, 6 papers with code

On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learning

no code implementations16 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.

Data Augmentation Self-Supervised Learning

When less is more: Simplifying inputs aids neural network understanding

no code implementations14 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.

Dataset Condensation

Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features

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.

Anomaly Detection

Machine-Learning-Based Diagnostics of EEG Pathology

1 code implementation11 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.

EEG Machine Learning

Deep Invertible Networks for EEG-based brain-signal decoding

no code implementations17 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.

EEG

Training Generative Reversible Networks

1 code implementation5 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.

EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals

2 code implementations5 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.

Data Augmentation EEG +1

Hierarchical internal representation of spectral features in deep convolutional networks trained for EEG decoding

no code implementations21 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.

EEG Eeg Decoding +1

The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks

no code implementations16 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.

EEG Eeg Decoding +1

Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

2 code implementations26 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.

EEG

Brain Responses During Robot-Error Observation

no code implementations4 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.

EEG

Acting Thoughts: Towards a Mobile Robotic Service Assistant for Users with Limited Communication Skills

no code implementations20 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.

Deep learning with convolutional neural networks for EEG decoding and visualization

5 code implementations15 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

EEG Eeg Decoding

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