Differential Geometric View of Information Flow in Neural Nets

In this paper, we explore a space-time geometric view of signal representation in machine learning models. The question we are interested in is if we can identify what is causing signal representation errors – training data inadequacies, model insufficiencies, or both. Loosely expressed, this problem is stylistically similar to blind deconvolution problems. However, studies of space-time geometries might be able to partially solve this problem by considering the curvature produced by mass in (Anti-)de Sitter space. We study the effectiveness of our approach on the MNIST dataset.