activity
20212024
most citedDeep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification

3 citations · 4 across the 8 of their papers we have counts for

collaborators

8 papers

cs.NE2024

Variable Metric Evolution Strategies for High-dimensional Multi-Objective Optimization

Tobias Glasmachers

We design a class of variable metric evolution strategies well suited for high-dimensional problems. We target problems with many variables, not (necessarily) with many objectives.…

cs.NE2024

Deep-learning-based identification of individual motion characteristics from upper-limb trajectories towards disorder stage evaluation

Tim Sziburis, Susanne Blex, Tobias Glasmachers +1

The identification of individual movement characteristics sets the foundation for the assessment of personal rehabilitation progress and can provide diagnostic information on level…

q-bio.NC2024

GET: A Generative EEG Transformer for Continuous Context-Based Neural Signals

Omair Ali, Muhammad Saif-ur-Rehman, Marita Metzler +3

Generating continuous electroencephalography (EEG) signals through advanced artificial neural networks presents a novel opportunity to enhance brain-computer interface (BCI) techno…

q-bio.QM20231 cited

Ruhr Hand Motion Catalog of Human Center-Out Transport Trajectories in 3D Task-Space Captured by a Redundant Measurement System

Tim Sziburis, Susanne Blex, Tobias Glasmachers +1

Neurological conditions are a major source of movement disorders. Motion modelling and variability analysis have the potential to identify pathology but require profound data. We i…

cs.LG2023

Leveraging Topological Maps in Deep Reinforcement Learning for Multi-Object Navigation

Simon Hakenes, Tobias Glasmachers

This work addresses the challenge of navigating expansive spaces with sparse rewards through Reinforcement Learning (RL). Using topological maps, we elevate elementary actions to o…

cs.LG2023

Understanding Activation Patterns in Artificial Neural Networks by Exploring Stochastic Processes

Stephan Johann Lehmler, Muhammad Saif-ur-Rehman, Tobias Glasmachers +1

To gain a deeper understanding of the behavior and learning dynamics of (deep) artificial neural networks, it is valuable to employ mathematical abstractions and models. These tool…