Reviews: Insights on representational similarity in neural networks with canonical correlation

EDIT: The authors response addresses my concerns about reproducibility and the appendix they promise should contain the missing details of their setup. Summary and relation to previous work: This paper presents projection weighted canonical correlation analysis (CCA) as a method to interrogate neural network representations. It is a direct continuation of previous work on singular vector canonical correlation analysis (SVCCA), addressing several limitations of SVCCA and applying the method to new models and questions. They observe that some CCA components stabilize early in training and remain stable (signal) while other components continue to vary even after performance has converged (noise). This motivates replacing the mean canonical correlation coefficient (used as a similarity metric in SVCCA) with a weighted mean, where components that better explain the activation patterns receive a higher weight.