An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications

Autoencoders are a self-supervised learning system where, during training, the output is an approximation of the input. Typically, autoencoders have three parts: Encoder (which produces a compressed latent space representation of the input data), the Latent Space (which retains the knowledge in the...

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Bibliographic Details
Published in:	Entropy (Basel, Switzerland) Vol. 24; no. 1; p. 55
Main Authors:	Singh, Aman, Ogunfunmi, Tokunbo
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 28.12.2021 MDPI
Subjects:	Bayesian analysis Coders Datasets Deep learning EEG Electrocardiography EMG Entropy Finance Generative adversarial networks Image classification Image enhancement Information theory Machine learning Object recognition Probability distribution Random variables Review Separation Signal processing Speech speech source separation variational autoencoders volatility surfaces deep learning variational autoencoders EEG ECG generative models volatility surfaces speech source separation EMG
ISSN:	1099-4300, 1099-4300
Online Access:	Get full text
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