AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work presents a principled analysis of zero-shot task generalisation in the offline setting and conducts an extensive empirical investigation into the scaling behaviour governing task diversity, dataset size, and network capacity.
One property of the input is turned into a prediction of whether a network learns memory and why correlated data turns recurrent networks into change detectors and explains why correlated data turns recurrent networks into change detectors.
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