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

Kernel normalization methods have been employed to improve robustness ofoptimization methods to reparametrization of convolution kernels, covariateshift, and to accelerate training of Convolutional Neural Networks (CNNs).However, our understanding of theoretical properties of these methods haslagged behind their success in applications. We develop a geometric frameworkto elucidate underlying mechanisms of a diverse range of kernel normalizationmethods. Our framework enables us to expound and identify geometry of space ofnormalized kernels. We analyze and delineate how state-of-the-art kernelnormalization methods affect the geometry of search spaces of the stochasticgradient descent (SGD) algorithms in CNNs. Following our theoretical results,we propose a SGD algorithm with assurance of almost sure convergence of themethods to a solution at single minimum of classification loss of CNNs.Experimental results show that the proposed method achieves state-of-the-artperformance for major image classification benchmarks with CNNs.

Optimization on Submanifolds of Convolution Kernels in CNNs