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

We introduce a novel dictionary optimization method for high-dimensionalvector quantization employed in approximate nearest neighbor (ANN) search.Vector quantization methods first seek a series of dictionaries, thenapproximate each vector by a sum of elements selected from these dictionaries.An optimal series of dictionaries should be mutually independent, and eachdictionary should generate a balanced encoding for the target dataset. Existingmethods did not explicitly consider this. To achieve these goals along withminimizing the quantization error (residue), we propose a novel dictionaryoptimization method called \emph{Dictionary Annealing} that alternatively"heats up" a single dictionary by generating an intermediate dataset withresidual vectors, "cools down" the dictionary by fitting the intermediatedataset, then extracts the new residual vectors for the next iteration. Bettercodes can be learned by DA for the ANN search tasks. DA is easily implementedon GPU to utilize the latest computing technology, and can easily extended toan online dictionary learning scheme. We show by experiments that our optimizeddictionaries substantially reduce the overall quantization error. Jointly usedwith residual vector quantization, our optimized dictionaries lead to a betterapproximate nearest neighbor search performance compared to thestate-of-the-art methods.

Learning Better Encoding for Approximate Nearest Neighbor Search with Dictionary Annealing