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

Inference in log-linear models scales linearly in the size of output space inthe worst-case. This is often a bottleneck in natural language processing andcomputer vision tasks when the output space is feasibly enumerable but verylarge. We propose a method to perform inference in log-linear models withsublinear amortized cost. Our idea hinges on using Gumbel random variableperturbations and a pre-computed Maximum Inner Product Search data structure toaccess the most-likely elements in sublinear amortized time. Our method yieldsprovable runtime and accuracy guarantees. Further, we present empiricalexperiments on ImageNet and Word Embeddings showing significant speedups forsampling, inference, and learning in log-linear models.

Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search