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

Perturb-and-MAP offers an elegant approach to approximately sample from aenergy-based model (EBM) by computing the maximum-a-posteriori (MAP)configuration of a perturbed version of the model. Sampling in turn enableslearning. However, this line of research has been hindered by the generalintractability of the MAP computation. Very few works venture outside tractablemodels, and when they do, they use linear programming approaches, which as wewill show, have several limitations. In this work we presentperturb-and-max-product (PMP), a parallel and scalable mechanism for samplingand learning in discrete EBMs. Models can be arbitrary as long as they arebuilt using tractable factors. We show that (a) for Ising models, PMP is ordersof magnitude faster than Gibbs and Gibbs-with-Gradients (GWG) at learning andgenerating samples of similar or better quality; (b) PMP is able to learn andsample from RBMs; (c) in a large, entangled graphical model in which Gibbs andGWG fail to mix, PMP succeeds.

Perturb-and-max-product: Sampling and learning in discrete energy-based models