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

Large-scale pre-trained language models have shown remarkable results indiverse NLP applications. Unfortunately, these performance gains have beenaccompanied by a significant increase in computation time and model size,stressing the need to develop new or complementary strategies to increase theefficiency of these models. In this paper we propose DACT-BERT, adifferentiable adaptive computation time strategy for BERT-like models.DACT-BERT adds an adaptive computational mechanism to BERT's regular processingpipeline, which controls the number of Transformer blocks that need to beexecuted at inference time. By doing this, the model learns to combine the mostappropriate intermediate representations for the task at hand. Our experimentsdemonstrate that our approach, when compared to the baselines, excels on areduced computational regime and is competitive in other less restrictive ones.

DACT-BERT: Differentiable Adaptive Computation Time for an Efficient BERT Inference