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

This work develops problem statements related to encoders and autoencoderswith the goal of elucidating variational formulations and establishing clearconnections to information-theoretic concepts. Specifically, four problems withvarying levels of input are considered : a) The data, likelihood and priordistributions are given, b) The data and likelihood are given; c) The data andprior are given; d) the data and the dimensionality of the parameters isspecified. The first two problems seek encoders (or the posterior) and thelatter two seek autoencoders (i.e. the posterior and the likelihood). Avariational Bayesian setting is pursued, and detailed derivations are providedfor the resulting optimization problem. Following this, a linear Gaussiansetting is adopted, and closed form solutions are derived. Numericalexperiments are also performed to verify expected behavior and assessconvergence properties. Explicit connections are made to rate-distortiontheory, information bottleneck theory, and the related concept of sufficiencyof statistics is also explored. One of the motivations of this work is topresent the theory and learning dynamics associated with variational inferenceand autoencoders, and to expose information theoretic concepts from acomputational science perspective.

Variational Encoders and Autoencoders : Information-theoretic Inference and Closed-form Solutions