prai-experiments
brycewang-stanford/Awesome-Journal-Skills
This guide provides a comprehensive checklist for designing, executing, and presenting empirical evidence for academic papers in Pattern Recognition and AI. It covers critical areas like establishing fair baselines, ensuring statistical rigor (mean ± std dev, significance testing), conducting sufficient ablation studies, preventing data leakage, and reporting computational costs. It helps authors make their findings withstand rigorous external review.
