paperswithcode/releasing-research-code
Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)
This resource provides clear recommendations for Machine Learning researchers and students on how to structure and present their code when publishing research papers. It guides you in preparing a comprehensive repository, ensuring your experimental setup, training procedures, and model evaluations are transparent and easily reproducible by others. The goal is to make your research findings credible and easy for the community to build upon.
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Use this if you are an ML researcher, student, or academic submitting code alongside a research paper and want to ensure it is well-received, reproducible, and impactful.
Not ideal if you are looking for guidance on general software development best practices for non-research-specific projects or if you're not involved in publishing academic ML research.
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May 19, 2023
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