AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes a swap-based local search that decreases the bound at every accepted step and can refine the output of any selector without worsening its bound, and provides experimental results on CIFAR10 and CIFAR100 showing that it matches existing data selection baselines.
It is suggested that simple in-training interpolation deserves consideration before introducing more elaborate forgetting-mitigation mechanisms, and Sequential Fine-tuning with Averaging with Averaging with SFA is introduced and evaluated.
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