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

In this paper, we hypothesize that internal function sharing is one of thereasons to weaken o.o.d. or systematic generalization in deep learning forclassification tasks. Under equivalent prediction, a model partitions an inputspace into multiple parts separated by boundaries. The function sharing prefersto reuse boundaries, leading to fewer parts for new outputs, which conflictswith systematic generalization. We show such phenomena in standard deeplearning models, such as fully connected, convolutional, residual networks,LSTMs, and (Vision) Transformers. We hope this study provides novel insightsinto systematic generalization and forms a basis for new research directions.

On a Built-in Conflict between Deep Learning and Systematic Generalization