AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Moderate-corruption finetuning increases corruption tolerance while simultaneously reducing displacement-normalized linearization error, associating improved robustness with a more nearly linear response to corruption.
Surprisingly, when evaluating a method by the best program it generates, it is found that learning model weights via ES is worse than invoking ES with a learning rate set to zero, i.e., using random weight-space perturbations to search for better models.
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