Context-Aware Learning for Generative Models

This work studies the class of algorithms for learning with side-information that emerges by extending generative models with embedded context-related variables. Using finite mixture models (FMMs) as the prototypical Bayesian network, we show that maximum-likelihood estimation (MLE) of parameters th...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 32; číslo 8; s. 3471 - 3483
Hlavní autori:	Perdikis, Serafeim, Leeb, Robert, Chavarriaga, Ricardo, Millan, Jose del R.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.08.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Approximation algorithms Artificial neural networks Bayesian analysis Brain-computer interfaces Classification Context Context awareness Context modeling Deep learning expectation–maximization (EM) Extremities finite mixture models (FMMs) Learning systems Machine learning Mathematical models maximum likelihood (ML) Maximum likelihood estimation Neural networks Parameter estimation Probabilistic logic Probabilistic models side information unsupervised learning variational autoencoder (VA)
ISSN:	2162-237X, 2162-2388, 2162-2388
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