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

Successfully training deep neural networks often requires either batchnormalization, appropriate weight initialization, both of which come with theirown challenges. We propose an alternative, geometrically motivated method fortraining. Using elementary results from linear programming, we introduce Farkaslayers: a method that ensures at least one neuron is active at a given layer.Focusing on residual networks with ReLU activation, we empirically demonstratea significant improvement in training capacity in the absence of batchnormalization or methods of initialization across a broad range of networksizes on benchmark datasets.

Farkas layers: don't shift the data, fix the geometry