Robust Nonparametric Hypothesis Testing to Understand Variability in Training Neural Networks

Sinjini Banerjee, Reilly Cannon, Tim Marrinan, Tony Chiang, Anand D. Sarwate

Abstract: Training a deep neural network (DNN) often involves stochastic optimization, which means each run will produce a different model. Several works suggest this variability is negligible when models have the same performance, which in the case of classification is test accuracy. However, models with similar test accuracy may not be computing the same function. We propose a new measure of closeness between classification models based on the output of the network before thresholding. Our measure is based on a robust hypothesis-testing framework and can be adapted to other quantities derived from trained models.