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

Neural Networks accomplish amazing things, but they suffer from computationaland memory bottlenecks that restrict their usage. Nowhere can this be betterseen than in the mobile space, where specialized hardware is being created justto satisfy the demand for neural networks. Previous studies have shown thatneural networks have vastly more connections than they actually need to dotheir work. This thesis develops a method that can compress networks to lessthan 10% of memory and less than 25% of computational power, without loss ofaccuracy, and without creating sparse networks that require special code torun.

Cramnet: Layer-wise Deep Neural Network Compression with Knowledge Transfer from a Teacher Network