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 propose using active learning based techniques to further improve thestate-of-the-art semi-supervised learning MixMatch algorithm. We provide athorough empirical evaluation of several active-learning and baseline methods,which successfully demonstrate a significant improvement on the benchmarkCIFAR-10, CIFAR-100, and SVHN datasets (as much as 1.5% in absolute accuracy).We also provide an empirical analysis of the cost trade-off betweenincrementally gathering more labeled versus unlabeled data. This analysis canbe used to measure the relative value of labeled/unlabeled data at differentpoints of the learning curve, where we find that although the incremental valueof labeled data can be as much as 20x that of unlabeled, it quickly diminishesto less than 3x once more than 2,000 labeled example are observed. Code can befound at https://github.com/google-research/mma.

Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels