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

We provide novel guaranteed approaches for training feedforward neuralnetworks with sparse connectivity. We leverage on the techniques developedpreviously for learning linear networks and show that they can also beeffectively adopted to learn non-linear networks. We operate on the momentsinvolving label and the score function of the input, and show that theirfactorization provably yields the weight matrix of the first layer of a deepnetwork under mild conditions. In practice, the output of our method can beemployed as effective initializers for gradient descent.

Provable Methods for Training Neural Networks with Sparse Connectivity