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

Solving program induction problems requires searching through an enormousspace of possibilities. DreamCoder is an inductive program synthesis systemthat, whilst solving problems, learns to simplify search in an iterativewake-sleep procedure. The cost of search is amortised by training a neuralsearch policy, reducing search breadth and effectively "compiling" usefulinformation to compose program solutions across tasks. Additionally, a libraryof program components is learnt to express discovered solutions in fewercomponents, reducing search depth. In DreamCoder, the neural search policy hasonly an indirect effect on the library learnt through the program solutions ithelps discover. We present an approach for library learning that directlyleverages the neural search policy, effectively "decompiling" its amortisedknowledge to extract relevant program components. This provides strongeramortised inference: the amortised knowledge learnt to reduce search breadth isnow also used to reduce search depth. We integrate our approach with DreamCoderand demonstrate faster domain proficiency with improved generalisation on arange of domains, particularly when fewer example solutions are available.

DreamDecompiler: Bayesian Program Learning by Decompiling Amortised Knowledge