  In this paper, we focus on quantifying model stability as a function of
random seed by investigating the effects of the induced randomness on model
performance and the robustness of the model in general. We specifically perform
a controlled study on the effect of random seeds on the behaviour of attention,
gradient-based and surrogate model based (LIME) interpretations. Our analysis
suggests that random seeds can adversely affect the consistency of models
resulting in counterfactual interpretations. We propose a technique called
Aggressive Stochastic Weight Averaging (ASWA)and an extension called
Norm-filtered Aggressive Stochastic Weight Averaging (NASWA) which improves the
stability of models over random seeds. With our ASWA and NASWA based
optimization, we are able to improve the robustness of the original model, on
average reducing the standard deviation of the model's performance by 72%.
