Topic: Hessian eigenspectrum

Neuroscientists are increasingly turning to the mathematical framework of artificial neural networks (ANNs) training for insights into biological learning mechanisms. This has motivated an influx of biologically plausible learning rules that approximate backpropagation [1-9]. Despite achieving impressive performance quantified by accuracy, these studies have not covered the breadth of solution characteristics found by these rules. In this work, we leverage established theoretical tools from deep learning to investigate the robustness of solutions, and gain insights into generalization properti