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

Although more layers and more parameters generally improve the accuracy ofthe models, such big models generally have high computational complexity andrequire big memory, which exceed the capacity of small devices for inferenceand incurs long training time. In addition, it is difficult to afford longtraining time and inference time of big models even in high performanceservers, as well. As an efficient approach to compress a large deep model (ateacher model) to a compact model (a student model), knowledge distillationemerges as a promising approach to deal with the big models. Existing knowledgedistillation methods cannot exploit the elastic available computing resourcesand correspond to low efficiency. In this paper, we propose an Elastic DeepLearning framework for knowledge Distillation, i.e., EDL-Dist. The advantagesof EDL-Dist are three-fold. First, the inference and the training process isseparated. Second, elastic available computing resources can be utilized toimprove the efficiency. Third, fault-tolerance of the training and inferenceprocesses is supported. We take extensive experimentation to show that thethroughput of EDL-Dist is up to 3.125 times faster than the baseline method(online knowledge distillation) while the accuracy is similar or higher.

Large-scale Knowledge Distillation with Elastic Heterogeneous Computing Resources