AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 2 of 17 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
SHOPCA is computationally tractable, parameter-efficient, and applicable to domains requiring fully unsupervised, geometry-aware dimensionality reduction, and surpasses UMAP on small-sample settings, where iterative neighborhood-based manifold estimation can degrade.
Curvature-Aware Radius Shrinkage for Adaptive Nearest Neighbor Classification (CARSANN) is introduced, a geometry-driven framework that adapts the spatial support of each neighborhood according to local geometric complexity and is competitive with adaptive nearest-neighbor methods.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.