A machine learning researcher is facing a dilemma when their reproduction of a published paper's results, using the authors' own code and hyperparameters, yields significantly lower numbers on two benchmarks. The researcher plans to present both the original published figures and their reproduced results, but is concerned that reviewers might dismiss their findings due to a bias towards trusting published numbers. They are seeking advice on how to convincingly present this discrepancy, whether to contact the original authors, and how to phrase it to avoid appearing accusatory while still highlighting their findings. AI
IMPACT Highlights challenges in scientific reproducibility within machine learning research.
RANK_REASON The cluster discusses a researcher's difficulty in reproducing results from a published paper, a common issue in the research community. [lever_c_demoted from research: ic=1 ai=1.0]
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