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• W3004456744 abstract "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: this https URL." @default.
• W3004456744 created "2020-02-14" @default.
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• W3004456744 date "2020-02-09" @default.
• W3004456744 modified "2023-09-27" @default.
• W3004456744 title "On the distance between two neural networks and the stability of learning" @default.
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• W3004456744 hasPublicationYear "2020" @default.
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