13 papers · 1 filter
Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time Series Forecasting Based on Biological ODEs
Christian Klötergens, Vijaya Krishna Yalavarthi, Randolf Scholz +3
State-of-the-art methods for forecasting irregularly sampled time series with missing values predominantly rely on just four datasets and a few small toy examples for evaluation. W…
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…
Mixing It Up: Exploring Mixer Networks for Irregular Multivariate Time Series Forecasting
Christian Klötergens, Tim Dernedde, Lars Schmidt-Thieme +1
Forecasting irregularly sampled multivariate time series with missing values (IMTS) is a fundamental challenge in domains such as healthcare, climate science, and biology. While re…
HPMixer: Hierarchical Patching for Multivariate Time Series Forecasting
Jung Min Choi, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme
In long-term multivariate time series forecasting, effectively capturing both periodic patterns and residual dynamics is essential. To address this within standard deep learning be…
Moco: A Learnable Meta Optimizer for Combinatorial Optimization
Tim Dernedde, Daniela Thyssens, Sören Dittrich +2
Relevant combinatorial optimization problems (COPs) are often NP-hard. While they have been tackled mainly via handcrafted heuristics in the past, advances in neural networks have…
The Role of Active Learning in Modern Machine Learning
Thorben Werner, Lars Schmidt-Thieme, Vijaya Krishna Yalavarthi
Even though Active Learning (AL) is widely studied, it is rarely applied in contexts outside its own scientific literature. We posit that the reason for this is AL's high computati…