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 develop information geometric techniques to understand the representationslearned by deep networks when they are trained on different tasks usingsupervised, meta-, semi-supervised and contrastive learning. We shed light onthe following phenomena that relate to the structure of the space of tasks: (1)the manifold of probabilistic models trained on different tasks using differentrepresentation learning methods is effectively low-dimensional; (2) supervisedlearning on one task results in a surprising amount of progress even onseemingly dissimilar tasks; progress on other tasks is larger if the trainingtask has diverse classes; (3) the structure of the space of tasks indicated byour analysis is consistent with parts of the Wordnet phylogenetic tree; (4)episodic meta-learning algorithms and supervised learning traverse differenttrajectories during training but they fit similar models eventually; (5)contrastive and semi-supervised learning methods traverse trajectories similarto those of supervised learning. We use classification tasks constructed fromthe CIFAR-10 and Imagenet datasets to study these phenomena.

A picture of the space of typical learnable tasks