Multi-perspective embedding for non-metric time series classification. - Maximilian Münch; Simon Heilig; Frank-Michael Schleif (ESANN, 2021)

Scalable embedding of multiple perspectives for indefinite life-science data analysis. - Maximilian Münch; Simon Heilig; Philipp Väth; Frank-Michael Schleif (SSCI, 2021)

Revisiting Memory Efficient Kernel Approximation: An Indefinite Learning Perspective. - Simon Heilig; Maximilian Münch; Frank-Michael Schleif (CoRR, 2021)

Adaptive multi-modal positive semi-definite and indefinite kernel fusion for binary classification. - Maximilian Münch; Christoph Raab; Simon Heilig; Manuel Röder; Frank-Michael Schleif (ESANN, 2022)

Memory Efficient Kernel Approximation for Non-Stationary and Indefinite Kernels. - Simon Heilig; Maximilian Münch; Frank-Michael Schleif (IJCNN, 2022)

Static and adaptive subspace information fusion for indefinite heterogeneous proximity data. - Maximilian Münch; Manuel Röder; Simon Heilig; Christoph Raab; Frank-Michael Schleif (Neurocomputing, 2023)

Injecting Hamiltonian Architectural Bias into Deep Graph Networks for Long-Range Propagation. - Simon Heilig; Alessio Gravina; Alessandro Trenta; Claudio Gallicchio; Davide Bacciu (CoRR, 2024)

Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks. - Simon Heilig; Alessio Gravina; Alessandro Trenta; Claudio Gallicchio; Davide Bacciu (ICLR, 2025)

Towards an Optimal Control Perspective of ResNet Training. - Jens Püttschneider; Simon Heilig; Asja Fischer; Timm Faulwasser (CoRR, 2025)