Skill UI

A comprehensive guide for hardening deep learning papers, particularly in Computer Vision (CVPR). It details best practices for achieving rigorous reproducibility by utilizing the Compute Reporting Form (CRF), maintaining a detailed recipe ledger, ensuring benchmark split hygiene, and meticulously documenting all training hyperparameters, seeds, and variance analyses for academic publication.

A comprehensive methodology guide for achieving maximum checkability in theoretical computer science proofs, especially for high-stakes academic venues like FOCS. It outlines five critical practices: maintaining a hypothesis ledger, freezing theorem statements across all document versions, auditing external theorems, providing certificates for computed steps, and handling errata formally. This ensures proofs withstand intense academic scrutiny.