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

Deep neural networks have gained tremendous popularity in last few years.They have been applied for the task of classification in almost every domain.Despite the success, deep networks can be incredibly slow to train for evenmoderate sized models on sufficiently large datasets. Additionally, thesenetworks require large amounts of data to be able to generalize. The importanceof speeding up convergence, and generalization in deep networks can not beoverstated. In this work, we develop an optimization algorithm based ongeneralized-optimal updates derived from minibatches that lead to fasterconvergence. Towards the end, we demonstrate on two benchmark datasets that theproposed method achieves two orders of magnitude speed up over traditionalback-propagation, and is more robust to noise/over-fitting.

Faster Convergence & Generalization in DNNs