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

We report a series of robust empirical observations, demonstrating that deepNeural Networks learn the examples in both the training and test sets in asimilar order. This phenomenon is observed in all the commonly used benchmarkswe evaluated, including many image classification benchmarks, and one textclassification benchmark. While this phenomenon is strongest for models of thesame architecture, it also crosses architectural boundaries -- models ofdifferent architectures start by learning the same examples, after which themore powerful model may continue to learn additional examples. We further showthat this pattern of results reflects the interplay between the way neuralnetworks learn benchmark datasets. Thus, when fixing the architecture, we showsynthetic datasets where this pattern ceases to exist. When fixing the dataset,we show that other learning paradigms may learn the data in a different order.We hypothesize that our results reflect how neural networks discover structurein natural datasets.

Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets