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

This paper provides a unifying view of a wide range of problems of interestin machine learning by framing them as the minimization of functionals definedon the space of probability measures. In particular, we show that generativeadversarial networks, variational inference, and actor-critic methods inreinforcement learning can all be seen through the lens of our framework. Wethen discuss a generic optimization algorithm for our formulation, calledprobability functional descent (PFD), and show how this algorithm recoversexisting methods developed independently in the settings mentioned earlier.

Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning