AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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TabFM, a 400M-parameter tabular foundation model that formulates supervised tabular prediction as in-context learning, produces calibrated zero-shot predictions in a single forward pass without task-specific tuning.
EFE demonstrates that LLM-based evolution can improve both accuracy and interpretability when automatically tackling structured data, and is found to be particularly effective on classical decision trees.
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