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

Transferring knowledge from prior source tasks in solving a new target taskcan be useful in several learning applications. The application of transferposes two serious challenges which have not been adequately addressed. First,the agent should be able to avoid negative transfer, which happens when thetransfer hampers or slows down the learning instead of helping it. Second, theagent should be able to selectively transfer, which is the ability to selectand transfer from different and multiple source tasks for different parts ofthe state space of the target task. We propose A2T (Attend, Adapt andTransfer), an attentive deep architecture which adapts and transfers from thesesource tasks. Our model is generic enough to effect transfer of either policiesor value functions. Empirical evaluations on different learning algorithms showthat A2T is an effective architecture for transfer by being able to avoidnegative transfer while transferring selectively from multiple source tasks inthe same domain.

Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain