Papers
1–3 of 3Research Paper·Feb 26, 2026
RepSPD: Enhancing SPD Manifold Representation in EEGs via Dynamic Graphs
Decoding brain activity from electroencephalography (EEG) is crucial for neuroscience and clinical applications. Among recent advances in deep learning for EEG, geometric learning stands out as its th...
6.0 viability
Research Paper·Feb 16, 2026
PolyNODE: Variable-dimension Neural ODEs on M-polyfolds
Neural ordinary differential equations (NODEs) are geometric deep learning models based on dynamical systems and flows generated by vector fields on manifolds. Despite numerous successful applications...
5.0 viability
Research Paper·Feb 16, 2026
Spectral Convolution on Orbifolds for Geometric Deep Learning
Geometric deep learning (GDL) deals with supervised learning on data domains that go beyond Euclidean structure, such as data with graph or manifold structure. Due to the demand that arises from appli...
2.0 viability