Survey of machine-learning experimental methods at NeurIPS2019 and ICLR2020
user-5f8cf7e04c775ec6fa691c92(2020)
摘要
How do machine-learning researchers run their empirical validation? In the context of a push for improved reproducibility and benchmarking, this question is important to develop new tools for model comparison. This document summarizes a simple survey about experimental procedures, sent to authors of published papers at two leading conferences, NeurIPS 2019 and ICLR 2020. It gives a simple picture of how hyper-parameters are set, how many baselines and datasets are included, or how seeds are used.
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关键词
neurips2019,machine-learning machine-learning,experimental methods
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