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

Deep learning is typically performed by learning a neural network solely fromdata in the form of input-output pairs ignoring available domain knowledge. Inthis work, the Constraint Guided Gradient Descent (CGGD) framework is proposedthat enables the injection of domain knowledge into the training procedure. Thedomain knowledge is assumed to be described as a conjunction of hard inequalityconstraints which appears to be a natural choice for several applications.Compared to other neuro-symbolic approaches, the proposed method converges to amodel that satisfies any inequality constraint on the training data and doesnot require to first transform the constraints into some ad-hoc term that isadded to the learning (optimisation) objective. Under certain conditions, it isshown that CGGD can converges to a model that satisfies the constraints on thetraining set, while prior work does not necessarily converge to such a model.It is empirically shown on two independent and small data sets that CGGD makestraining less dependent on the initialisation of the network and improves theconstraint satisfiability on all data.

Constraint Guided Gradient Descent: Guided Training with Inequality Constraints