AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The results suggest that the architectural prior provides a useful organization of contextual information, while exposure to missing-modality cases is still needed for robust fallback when auxiliary inputs are absent.
It is shown that a key factor is the error correlation structure between human and AI predictions, and when the AI's prediction errors are negatively correlated with those of the human, the decision maker can construct robust strategies which guarantee improvements in expected utility.
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