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

Mixture-of-Expert (MoE) models have obtained state-of-the-art performance inNeural Machine Translation (NMT) tasks. Existing works in MoE mostly consider ahomogeneous design where the same number of experts of the same size are placeduniformly throughout the network. Furthermore, existing MoE works do notconsider computational constraints (e.g., FLOPs, latency) to guide theirdesign. To this end, we develop AutoMoE -- a framework for designingheterogeneous MoE's under computational constraints. AutoMoE leverages NeuralArchitecture Search (NAS) to obtain efficient sparse MoE sub-transformers with4x inference speedup (CPU) and FLOPs reduction over manually designedTransformers, with parity in BLEU score over dense Transformer and within 1BLEU point of MoE SwitchTransformer, on aggregate over benchmark datasets forNMT. Heterogeneous search space with dense and sparsely activated Transformermodules (e.g., how many experts? where to place them? what should be theirsizes?) allows for adaptive compute -- where different amounts of computationsare used for different tokens in the input. Adaptivity comes naturally fromrouting decisions which send tokens to experts of different sizes. AutoMoEcode, data, and trained models are available at https://aka.ms/AutoMoE.

AutoMoE: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine Translation