AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Experiments across synthetic benchmarks and a real-world robot arm control problem, together with evaluations under different acquisition functions and evolutionary multitasking, demonstrate the effectiveness and generality of ICG-MTO for few-shot multitask optimization.
This work proposes an unsupervised PDE-based finetuning framework that eliminates the need for ground-truth solutions, and introduces NSLoRA, a Newton-Schulz orthogonalized variant that rebalances adaptation.
This paper model MTO as a sequence of sequential transfer optimization problems, concentrating evaluations on a single target per iteration, and proposes a likelihood-informed task prioritization mechanism to maximize transfer utility by identifying the task most likely ready for knowledge integration.
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