AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Wasserstein gradient flow extends gradient descent to probability measures. Its Hessian-guided perturbed variant (PWGF) adds Gaussian perturbations to escape saddle points in nonconvex problems. We investigate when its approximation by finitely many interacting particles remains accurate over growing time horizons. Our...
Generative foundation models are attracting interest for their ability to produce desired outputs from demonstrations given at inference time, without updating parameters. However, since a few demonstrations cannot uniquely identify the intended task, the challenge is how to learn and sample from an output distribution...
This work considers discrete distributions with hidden forest structure, accessed through a fixed approximate conditional oracle, and studies whether total evaluations, including discovery, can be sublinear in sequence length $N; sublinear sequential depth then follows.
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