106 citations · 129 across the 7 of their papers we have counts for
7 papers
A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks
Maximilian Baronig, Yeganeh Bahariasl, Ozan Özdenizci +1
Recurrent spiking neural networks (RSNNs) can be implemented very efficiently in neuromorphic systems. Nevertheless, training of these models with powerful gradient-based learning…
Privacy-Aware Lifelong Learning
Ozan Özdenizci, Elmar Rueckert, Robert Legenstein
Lifelong learning algorithms enable models to incrementally acquire new knowledge without forgetting previously learned information. Contrarily, the field of machine unlearning foc…
TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning
Philipp Hallgarten, David Bethge, Ozan Özdenizci +2
Limited availability of labeled physiological data often prohibits the use of powerful supervised deep learning models in the biomedical machine intelligence domain. We approach th…
Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models
Ozan Özdenizci, Robert Legenstein
Image restoration under adverse weather conditions has been of significant interest for various computer vision applications. Recent successful methods rely on the current progress…
Domain-Invariant Representation Learning from EEG with Private Encoders
David Bethge, Philipp Hallgarten, Tobias Grosse-Puppendahl +4
Deep learning based electroencephalography (EEG) signal processing methods are known to suffer from poor test-time generalization due to the changes in data distribution. This beco…
Personalized Brain-Computer Interface Models for Motor Rehabilitation
Anastasia-Atalanti Mastakouri, Sebastian Weichwald, Ozan Özdenizci +3
We propose to fuse two currently separate research lines on novel therapies for stroke rehabilitation: brain-computer interface (BCI) training and transcranial electrical stimulati…
Causal interpretation rules for encoding and decoding models in neuroimaging
Sebastian Weichwald, Timm Meyer, Ozan Özdenizci +3
Causal terminology is often introduced in the interpretation of encoding and decoding models trained on neuroimaging data. In this article, we investigate which causal statements a…