  This paper relates parameter distance to gradient breakdown for a broad class
of nonlinear compositional functions. The analysis leads to a new distance
function called deep relative trust and a descent lemma for neural networks.
Since the resulting learning rule seems to require little to no learning rate
tuning, it may unlock a simpler workflow for training deeper and more complex
neural networks. The Python code used in this paper is here:
https://github.com/jxbz/fromage.
