AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces structured dimension-matched variational transdimensional inference (SM-VTI) for finite enumerable model spaces and develops a rooted construction graph that expresses a model as a sequence of local stop/child decisions.
Experiments on logistic regression, probabilistic matrix factorization, and Bayesian neural networks show that HyperMC improves posterior approximation or predictive calibration relative to MAMBA, grid search, and heuristic baselines, while Robust HyperMC yields more stable and reproducible tuning results.
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