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 meta-learning an agent extracts knowledge from observed tasks, aiming tofacilitate learning of novel future tasks. Under the assumption that futuretasks are 'related' to previous tasks, the accumulated knowledge should belearned in a way which captures the common structure across learned tasks,while allowing the learner sufficient flexibility to adapt to novel aspects ofnew tasks. We present a framework for meta-learning that is based ongeneralization error bounds, allowing us to extend various PAC-Bayes bounds tometa-learning. Learning takes place through the construction of a distributionover hypotheses based on the observed tasks, and its utilization for learning anew task. Thus, prior knowledge is incorporated through setting anexperience-dependent prior for novel tasks. We develop a gradient-basedalgorithm which minimizes an objective function derived from the bounds anddemonstrate its effectiveness numerically with deep neural networks. Inaddition to establishing the improved performance available throughmeta-learning, we demonstrate the intuitive way by which prior information ismanifested at different levels of the network.

Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory