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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We propose a learning paradigm for numerical approximation of differentialinvariants of planar curves. Deep neural-networks' (DNNs) universalapproximation properties are utilized to estimate geometric measures. Theproposed framework is shown to be a preferable alternative to axiomaticconstructions. Specifically, we show that DNNs can learn to overcomeinstabilities and sampling artifacts and produce numerically-stable signaturesfor curves subject to a given group of transformations in the plane. We comparethe proposed schemes to alternative state-of-the-art axiomatic constructions ofgroup invariant arc-lengths and curvatures.

Deep Signatures -- Learning Invariants of Planar Curves