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

Recent work on mini-batch consistency (MBC) for set functions has broughtattention to the need for sequentially processing and aggregating chunks of apartitioned set while guaranteeing the same output for all partitions. However,existing constraints on MBC architectures lead to models with limitedexpressive power. Additionally, prior work has not addressed how to deal withlarge sets during training when the full set gradient is required. To addressthese issues, we propose a Universally MBC (UMBC) class of set functions whichcan be used in conjunction with arbitrary non-MBC components while stillsatisfying MBC, enabling a wider range of function classes to be used in MBCsettings. Furthermore, we propose an efficient MBC training algorithm whichgives an unbiased approximation of the full set gradient and has a constantmemory overhead for any set size for both train- and test-time. We conductextensive experiments including image completion, text classification,unsupervised clustering, and cancer detection on high-resolution images toverify the efficiency and efficacy of our scalable set encoding framework. Ourcode is available at github.com/jeffwillette/umbc

Scalable Set Encoding with Universal Mini-Batch Consistency and Unbiased Full Set Gradient Approximation