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

Like most learning algorithms, the multilayer perceptrons (MLP) is designedto learn a vector of parameters from data. However, in certain scenarios we areinterested in learning structured parameters (predictions) in the form ofsymmetric positive definite matrices. Here, we introduce a variant of the MLP,referred to as the matrix MLP, that is specialized at learning symmetricpositive definite matrices. We also present an application of the model withinthe context of the variational autoencoder (VAE). Our formulation of the VAEextends the vanilla formulation to the cases where the recognition and thegenerative networks can be from the parametric family of distributions withdense covariance matrices. Two specific examples are discussed in more detail:the dense covariance Gaussian and its generalization, the power exponentialdistribution. Our new developments are illustrated using both synthetic andreal data.

Constructing the Matrix Multilayer Perceptron and its Application to the VAE