English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Whereas it is believed that techniques such as Adam, batch normalization and,more recently, SeLU nonlinearities "solve" the exploding gradient problem, weshow that this is not the case in general and that in a range of popular MLParchitectures, exploding gradients exist and that they limit the depth to whichnetworks can be effectively trained, both in theory and in practice. We explainwhy exploding gradients occur and highlight the *collapsing domain problem*,which can arise in architectures that avoid exploding gradients.  ResNets have significantly lower gradients and thus can circumvent theexploding gradient problem, enabling the effective training of much deepernetworks. We show this is a direct consequence of the Pythagorean equation. Bynoticing that *any neural network is a residual network*, we devise the*residual trick*, which reveals that introducing skip connections simplifiesthe network mathematically, and that this simplicity may be the major cause fortheir success.

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