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

Most stochastic gradient descent algorithms can optimize neural networks thatare sub-differentiable in their parameters; however, this implies that theneural network's activation function must exhibit a degree of continuity whichlimits the neural network model's uniform approximation capacity to continuousfunctions. This paper focuses on the case where the discontinuities arise fromdistinct sub-patterns, each defined on different parts of the input space. Wepropose a new discontinuous deep neural network model trainable via a decoupledtwo-step procedure that avoids passing gradient updates through the network'sonly and strategically placed, discontinuous unit. We provide approximationguarantees for our architecture in the space of bounded continuous functionsand universal approximation guarantees in the space of piecewise continuousfunctions which we introduced herein. We present a novel semi-supervisedtwo-step training procedure for our discontinuous deep learning model, tailoredto its structure, and we provide theoretical support for its effectiveness. Theperformance of our model and trained with the propose procedure is evaluatedexperimentally on both real-world financial datasets and synthetic datasets.

Learning Sub-Patterns in Piecewise Continuous Functions