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

No single Automatic Differentiation (AD) system is the optimal choice for allproblems. This means informed selection of an AD system and combinations can bea problem-specific variable that can greatly impact performance. In the Juliaprogramming language, the major AD systems target the same input and thus intheory can compose. Hitherto, switching between AD packages in the JuliaLanguage required end-users to familiarize themselves with the user-facing APIof the respective packages. Furthermore, implementing a new, usable AD packagerequired AD package developers to write boilerplate code to define convenienceAPI functions for end-users. As a response to these issues, we presentAbstractDifferentiation.jl for the automatized generation of an extensive,unified, user-facing API for any AD package. By splitting the complexitybetween AD users and AD developers, AD package developers only need toimplement one or two primitive definitions to support various utilities for ADusers like Jacobians, Hessians and lazy product operators from nativeprimitives such as pullbacks or pushforwards, thus removing tedious -- but sofar inevitable -- boilerplate code, and enabling the easy switching andcomposing between AD implementations for end-users.

AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia