AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A new method is proposed, Multiple Embedding Replay Selection, MERS, which replaces the buffer selection module with a graph-based approach that integrates both supervised and self-supervised embeddings.
VISTA is introduced, an online self-distillation framework that enforces consistency along the optimization trajectory, using a validation-informed Marginal Coverage score to identify expert anchors, which are earlier model states that retain specialized competence over distinct data regions.
VAST (Veracity-Aware Semi-Supervised Training) outperforms the strongest graph-based SSL baselines at every operating point across three datasets, with statistically significant gains in 7 of 9 comparisons, while producing a deployable inductive classifier rather than requiring transductive graph inference.
A new FAL framework is proposed that utilizes federated representation learning to align client data in a shared embedding space that achieves performance that surpasses existing FAL methods even when they are given substantially larger annotation budgets, demonstrating the value of centralized coordination under priva...
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