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

Adaptive gradient methods, which adopt historical gradient information toautomatically adjust the learning rate, despite the nice property of fastconvergence, have been observed to generalize worse than stochastic gradientdescent (SGD) with momentum in training deep neural networks. This leaves howto close the generalization gap of adaptive gradient methods an open problem.In this work, we show that adaptive gradient methods such as Adam, Amsgrad, aresometimes "over adapted". We design a new algorithm, called Partially adaptivemomentum estimation method, which unifies the Adam/Amsgrad with SGD byintroducing a partial adaptive parameter $p$, to achieve the best from bothworlds. We also prove the convergence rate of our proposed algorithm to astationary point in the stochastic nonconvex optimization setting. Experimentson standard benchmarks show that our proposed algorithm can maintain a fastconvergence rate as Adam/Amsgrad while generalizing as well as SGD in trainingdeep neural networks. These results would suggest practitioners pick upadaptive gradient methods once again for faster training of deep neuralnetworks.

Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks