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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this work we propose the use of adaptive stochastic search as a buildingblock for general, non-convex optimization operations within deep neuralnetwork architectures. Specifically, for an objective function located at somelayer in the network and parameterized by some network parameters, we employadaptive stochastic search to perform optimization over its output. Thisoperation is differentiable and does not obstruct the passing of gradientsduring backpropagation, thus enabling us to incorporate it as a component inend-to-end learning. We study the proposed optimization module's properties andbenchmark it against two existing alternatives on a synthetic energy-basedstructured prediction task, and further showcase its use in stochastic optimalcontrol applications.

NOVAS: Non-convex Optimization via Adaptive Stochastic Search for End-to-End Learning and Control