GenusSink delivers near-linear-time approximate generalized Sinkhorn algorithms for bounded-genus graphs via separator decompositions, computational geometry, and fast matrix-vector multiplies with generalized distance matrices.
Wasserstein barycenter and its application to texture mixing
2 Pith papers cite this work. Polarity classification is still indexing.
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K-DSM uses per-feature kurtosis to set noise scales in DSM, enabling effective single-scale anomaly detection on tabular benchmarks in both semi-supervised and unsupervised settings.
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Near-Linear Time Generalized Sinkhorn Algorithms for Bounded Genus Graphs
GenusSink delivers near-linear-time approximate generalized Sinkhorn algorithms for bounded-genus graphs via separator decompositions, computational geometry, and fast matrix-vector multiplies with generalized distance matrices.
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Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection
K-DSM uses per-feature kurtosis to set noise scales in DSM, enabling effective single-scale anomaly detection on tabular benchmarks in both semi-supervised and unsupervised settings.