Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data

Abstract

In many machine learning applications, one uses pre-trained neural networks, having limited access to training and test data. Martin et al. show how to predict trends in the quality of such neural networks without access to this information, relevant for reproducibility, diagnostics, and validation.