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

Neural network architecture design mostly focuses on the new convolutionaloperator or special topological structure of network block, little attention isdrawn to the configuration of stacking each block, called Block Stacking Style(BSS). Recent studies show that BSS may also have an unneglectable impact onnetworks, thus we design an efficient algorithm to search it automatically. Theproposed method, AutoBSS, is a novel AutoML algorithm based on Bayesianoptimization by iteratively refining and clustering Block Stacking Style Code(BSSC), which can find optimal BSS in a few trials without biased evaluation.On ImageNet classification task, ResNet50/MobileNetV2/EfficientNet-B0 with oursearched BSS achieve 79.29%/74.5%/77.79%, which outperform the originalbaselines by a large margin. More importantly, experimental results on modelcompression, object detection and instance segmentation show the stronggeneralizability of the proposed AutoBSS, and further verify the unneglectableimpact of BSS on neural networks.

AutoBSS: An Efficient Algorithm for Block Stacking Style Search