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Published February 14, 2020 | Accepted Version
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On the distance between two neural networks and the stability of learning


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

Additional Information

The authors would like to thank Dillon Huff, Jeffrey Pennington and Florian Schaefer for useful conversations. They made heavy use of a codebase built by Jiahui Yu. They are much obliged to Sivakumar Arayandi Thottakara, Jan Kautz, Sabu Nadarajan and Nithya Natesan for infrastructure support. JB is supported by an NVIDIA fellowship.

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Accepted Version - 2002.03432.pdf


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