6 papers
NAE: Normalizing AutoEncoder
Muhammad Abdur Rafae, Niels Landwehr
We consider the setting of Normalizing flows with approximate inverses, an established paradigm spanning both full-dimensional () and bottleneck () settings, and group th…
SpikF-GO: Spiking Fourier Graph Operators for Multivariate Time Series Forecasting
Jafar Bakhshaliyev, Niels Landwehr
Spiking Neural Networks (SNNs) have emerged as an energy-efficient alternative to conventional neural networks, demonstrating strong performance in computer vision and robotics. Mo…
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…
HexFormer: Hyperbolic Vision Transformer with Exponential Map Aggregation
Haya Alyoussef, Ahmad Bdeir, Diego Coello de Portugal Mecke +3
Data across modalities such as images, text, and graphs often contains hierarchical and relational structures, which are challenging to model within Euclidean geometry. Hyperbolic…