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

This article presents a new search algorithm for the NP-hard problem ofoptimizing functions of binary variables that decompose according to agraphical model. It can be applied to models of any order and structure. Themain novelty is a technique to constrain the search space based on the topologyof the model. When pursued to the full search depth, the algorithm isguaranteed to converge to a global optimum, passing through a series ofmonotonously improving local optima that are guaranteed to be optimal within agiven and increasing Hamming distance. For a search depth of 1, it specializesto Iterated Conditional Modes. Between these extremes, a useful tradeoffbetween approximation quality and runtime is established. Experiments on modelsderived from both illustrative and real problems show that approximations foundwith limited search depth match or improve those obtained by state-of-the-artmethods based on message passing and linear programming.

The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search