Probabilistic meshless PDE methods extended to compact manifolds
The paper proves consistency and error bounds, then matches existing accuracy on cortical thickness data.
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Stories tagged #gaussian-processes
A General Kernel Framework for Non-CND Distance Measures Using |D|-Dimensional Sparse Landmark Embeddings
Proposes the Sparse Landmark Embedding kernel, guaranteeing PSD kernels for arbitrary distances like geodesic and Wasserstein without CND requirements.