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On the distance between two neural networks and the stability of learning

Jeremy Bernstein · Arash Vahdat · Yisong Yue · Ming-Yu Liu

Poster Session 1 #310


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:

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