AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces Population Fidelity, an evaluation framework that distinguishes key conditions required for a set of LLM-generated responses to represent a population and organizes these features and provides reusable code, data, and trained models for evaluating population fidelity across substantive domains and...
Label noise is common in medical imaging datasets due to factors such as inter-rater variability, annotation errors, and ambiguous cases. This can severely undermine the reliability and clinical effectiveness of machine learning models trained using those datasets. To address this challenge, we introduce Lightweight No...
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