The Machine Learning Reproducibility Checklist (v2.0, Apr.7 2020)
For all models andalgorithms presented, check if you include:
A clear description of the mathematical setting, algorithm, and/or model.A clear explanation of any assumptions.An analysis of the complexity (time, space, sample size) of any algorithm.
For all datasetsused, check if you include:
The relevant statistics, such as number of examples.- ~~The details of train / validation / test splits. ~~
An explanation of any data that were excluded, and all pre-processing step.A link to a downloadable version of the dataset or simulation environment.
For all shared code related to this work, check if you include:
Specification of dependencies.Training code.Evaluation code.(Pre-)trained model(s).README file includes table of results accompanied by precise command to run to produce those results.
For all reported experimental results, check if you include:
The range of hyper-parameters considered, method to select the best hyper-parameter configuration, and specification of all hyper-parameters used to generate results.The exact number of training and evaluation runs.A clear definition of the specific measure or statistics used to report results.A description of results with central tendency (e.g. mean) & variation (e.g. error bars).The average runtime for each result, or estimated energy cost.- ~~A description of the computing infrastructure used.~~~~