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This paper develops intrinsic dense associative memories on Riemannian manifolds by casting memory as Epanechnikov kernel-density mode seeking, showing that curvature—via Ricci curvature thresholds and geodesic capacity scalings—determines what such a memory retains, creates, and forgets, with demonstrations on WordNet noun hierarchy.
The paper proposes an amortized framework for learning bandwidth selection in kernel density estimation under logarithmic score, showing consistent performance gains over classical methods in various sampling scenarios.
Proposes Cascade-KDE, a training-free framework that uses two-dimensional kernel density estimation and truncated expectation to restore time-series corrupted by out-of-distribution impulse outliers while preserving local structure and derivative features.