The exploding gradient problem demystified - definition, prevalence, impact, origin, tradeoffs, and solutions

15 Dec 2017George PhilippDawn SongJaime G. Carbonell

Whereas it is believed that techniques such as Adam, batch normalization and, more recently, SeLU nonlinearities "solve" the exploding gradient problem, we show that this is not the case in general and that in a range of popular MLP architectures, exploding gradients exist and that they limit the depth to which networks can be effectively trained, both in theory and in practice. We explain why exploding gradients occur and highlight the *collapsing domain problem*, which can arise in architectures that avoid exploding gradients... (read more)

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