AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Bayesian fine-tuning experiments with LLMs and vision-language models support the proposed state-dependent heavy-tailed oracle model and show that RA-LMC matches or outperforms state-of-the-art baselines in accuracy and uncertainty quantification.
We study stochastic approximation of fixed points of a non-expansive operator $T$ when the oracle samples originate from a continuing Markovian trajectory. A direct block-minibatch implementation of Halpern iteration attains an expected last-iterate residual of order $O(\log N/N)$, but accrues a substantive complexity...
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