AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Under shared-history workloads, Cached-Linear substantially reduces computation and memory compared with Global-Softmax, while Balanced GDN improves selected direct-prediction endpoints with efficient context reuse.
This work proposes a framework with two complementary components: a permutation-invariant hierarchical module that captures interactions across features, operations, and abstraction levels, with a self-attention pooling mechanism that maps semantically equivalent structures to consistent embeddings aligned with downstr...
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