AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Activation space and parameter space provide complementary views of model computation. Activations represent information, while weights read, transform, and write that information. Yet existing interpretability methods largely study the two spaces separately, leaving the connection between represented information and p...
HARMONIA achieves stronger explanation recovery than existing interpretable graph baselines while maintaining competitive predictive performance and scaling to graphs with millions of nodes, and shows that interpretable graph learning can remain both faithful and scalable without sacrificing predictive effectiveness.
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