New paper bridges singular learning theory and information geometry

An arXiv preprint examines how singular learning theory and information geometry describe the same parameter spaces for machine learning models, but from different coordinate perspectives. The work focuses on the non-degeneracy assumptions behind information geometry, which overparameterised neural networks violate, and develops a geometric account of singular learning behaviour. The paper connects these frameworks to explain directions in parameter space that lose significance under model degeneracy.