AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This thesis builds on an existing diagnostics toolkit mainly for t-SNE and UMAP and turns it into a more accessible package for interested practitioners, while also extending it with diagnostics tools.
While FloDR only uses the first two output coordinates to create a two-dimensional embedding, it retains the remaining coordinates rather than discarding them, which enable diagnostic visualisations that are computed from the exact inverse of the model that drew the layout rather than from an approximate one.
This paper proposes visualizing the median of multiple NLDR outputs rather than relying on individual projections and finds that taking the median of multiple projections performs comparably to individual runs on multiple quality metrics, while increasing perturbation emphasizes global over local structure.
DMT-Dens is a parametric manifold-visualization method built on a latent-token Transformer encoder that optimizes a loss based on the Pearson correlation between k-nearest-neighbor log-radius estimates in the processed input and two-dimensional embedding spaces to preserve density.
A progressive multi-objective optimization framework is proposed that enhances t-SNE by integrating complementary loss functions, including a ranking-aware divergence (KLmax) and a Wasserstein-based term for global alignment.
It is found that sharpening improves preservation of the very nearest neighbors, while smoothing improves preservation of broader local neighborhoods, outperforming alternative affinity constructions including multiscale methods in the mid-local range.
The proposed method uses the L2 norm to directly map each multi-dimensional data point into a one-dimensional scalar value, which serves as a sorting criterion and is stored in a dictionary data structure to deterministically extract initial centroids.
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