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

The renaissance of artificial neural networks was catalysed by the success ofclassification models, tagged by the community with the broader term supervisedlearning. The extraordinary results gave rise to a hype loaded with ambitiouspromises and overstatements. Soon the community realised that the success owedmuch to the availability of thousands of labelled examples and supervisedlearning went, for many, from glory to shame: Some criticised deep learning asa whole and others proclaimed that the way forward had to be alternatives tosupervised learning: predictive, unsupervised, semi-supervised and, morerecently, self-supervised learning. However, all these seem brand names, ratherthan actual categories of a theoretically grounded taxonomy. Moreover, the callto banish supervised learning was motivated by the questionable claim thathumans learn with little or no supervision and are capable of robustout-of-distribution generalisation. Here, we review insights about learning andsupervision in nature, revisit the notion that learning and generalisation arenot possible without supervision or inductive biases and argue that we willmake better progress if we just call it by its name.

Rethinking supervised learning: insights from biological learning and from calling it by its name