AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Relational deep learning has emerged as a powerful paradigm for learning end-to-end predictive models over relational databases by representing them as temporal heterogeneous graphs. Recent advances based on graph neural networks and Transformers have achieved strong performance across diverse domains. However, the eff...
This work presents PolyStepOR, which trains directly from realized decision costs without pre-computed optima and extends to in-constraint predictions through repair or infeasibility penalties, and replaces optimal reference decisions and derivatives with forward evaluations.
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