AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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DIFFINT is proposed, an autoencoder whose latent bottleneck is structured as a set of soft, axis-aligned interval memberships learned end-to-end directly from raw numerical data, without any discretization or binarization.
WAND is an unsupervised tabular anomaly detector that is explainable by design, and attains the best mean Friedman rank at ROC-AUC parity with 16 unsupervised baselines, so the gain is interpretability at no accuracy cost.
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