Statistical Validity of Neural-Net Benchmarks

Abstract

Claims of better, faster or more efficient neural-net designs often hinge on low single digit percentage improvements (or less) in accuracy or speed compared to others. Current benchmark differences used for comparison have been based on a number of different metrics such as recall, the best of five-runs, the median of five runs, Top-1, Top-5, BLEU, ROC, RMS, etc. These metrics implicitly assert comparable distributions of metrics. Conspicuous by their absence are measures of statistical validity of these benchmark comparisons. This study examined neural-net benchmark metric distributions and determined there are researcher degrees of freedom that may affect comparison validity. An essay is developed and proposed for benchmarking and comparing reasonably expected neural-net performance metrics that minimizes researcher degrees of freedom. The essay includes an estimate of the effects and the interactions of hyper-parameter settings on the benchmark metrics of a neural-net as a measure of its optimization complexity.

Keywords

• Bayesian credible interval
• benchmark essay
• comparison
• factorial experiment
• hyper-parameters
• machine learning