6 papers
LAtte: Hyperbolic Lorentz Attention for Cross-Subject EEG Classification
Ahmad Bdeir, Johannes Burchert, Tom Hanika +2
Electroencephalogram (EEG) classification plays a key role in medical diagnosis and brain-computer interfaces, but remains challenging due to low signal-to-noise ratios and high in…
Temporal Patch Shuffle (TPS): Leveraging Patch-Level Shuffling to Boost Generalization and Robustness in Time Series Forecasting
Jafar Bakhshaliyev, Johannes Burchert, Niels Landwehr +1
Data augmentation is a crucial technique for improving model generalization and robustness, particularly in deep learning models where training data is limited. Although many augme…
Robust Hyperbolic Learning with Curvature-Aware Optimization
Ahmad Bdeir, Johannes Burchert, Lars Schmidt-Thieme +1
Hyperbolic deep learning has become a growing research direction in computer vision due to the unique properties afforded by the alternate embedding space. The negative curvature a…
Towards Comparable Active Learning
Thorben Werner, Johannes Burchert, Lars Schmidt-Thieme
Active Learning has received significant attention in the field of machine learning for its potential in selecting the most informative samples for labeling, thereby reducing data…
A Cross-Domain Benchmark for Active Learning
Thorben Werner, Johannes Burchert, Maximilian Stubbemann +1
Active Learning (AL) deals with identifying the most informative samples for labeling to reduce data annotation costs for supervised learning tasks. AL research suffers from the fa…
Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training
Johannes Burchert, Thorben Werner, Vijaya Krishna Yalavarthi +3
As with most other data domains, EEG data analysis relies on rich domain-specific preprocessing. Beyond such preprocessing, machine learners would hope to deal with such data as wi…