Probabilistic Autoencoder Using Fisher Information

Neural networks play a growing role in many scientific disciplines, including physics. Variational autoencoders (VAEs) are neural networks that are able to represent the essential information of a high dimensional data set in a low dimensional latent space, which have a probabilistic interpretation....

Full description

Saved in:

Bibliographic Details
Published in:	Entropy (Basel, Switzerland) Vol. 23; no. 12; p. 1640
Main Authors:	Zacherl, Johannes, Frank, Philipp, Enßlin, Torsten A.
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 06.12.2021 MDPI
Subjects:	Approximation Coders Cross correlation deep generative network Fisher information Fisher information metric Machine learning Neural networks Probability distribution Random variables Uncertainty variational autoencoder variational methods variational methods Fisher information metric deep generative network machine learning variational autoencoder
ISSN:	1099-4300, 1099-4300
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!