AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work develops a theoretical framework for characterizing the approximation and generalization errors of transformer-based operator learning, and derives a universal approximation error of transformer-based operator learning for H\"older-regular operators".
This work introduces a graph generation method that incorporates graph-theoretic principles into the learning process and preserves both global and local characteristics of the input graph while correcting the degree distribution to avoid duplicating the original topology.
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