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 perform an extensive empirical study of the statistical properties ofBatch Norm and other common normalizers. This includes an examination of thecorrelation between representations of minibatches, gradient norms, and Hessianspectra both at initialization and over the course of training. Through thisanalysis, we identify several statistical properties which appear linked toBatch Norm's superior performance. We propose two simple normalizers,PreLayerNorm and RegNorm, which better match these desirable properties withoutinvolving operations along the batch dimension. We show that PreLayerNorm andRegNorm achieve much of the performance of Batch Norm without requiring batchdependence, that they reliably outperform LayerNorm, and that they can beapplied in situations where Batch Norm is ineffective.

Is Batch Norm unique? An empirical investigation and prescription to emulate the best properties of common normalizers without batch dependence