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  2. School of Engineering
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  4. Publikationen School of Engineering
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dc.contributor.authorPerdikis, Serafeim-
dc.contributor.authorLeeb, Robert-
dc.contributor.authorChavarriaga, Ricardo-
dc.contributor.authorMillán, José del R.-
dc.date.accessioned2020-09-17T13:01:09Z-
dc.date.available2020-09-17T13:01:09Z-
dc.date.issued2020-08-10-
dc.identifier.issn2162-237Xde_CH
dc.identifier.issn2162-2388de_CH
dc.identifier.urihttps://repository.essex.ac.uk/28199/de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/20503-
dc.description.abstractThis work studies the class of algorithms for learning with side-information that emerge by extending generative models with embedded context-related variables. Using finite mixture models (FMM) as the prototypical Bayesian network, we show that maximum-likelihood estimation (MLE) of parameters through expectation-maximization (EM) improves over the regular unsupervised case and can approach the performances of supervised learning, despite the absence of any explicit ground truth data labeling. By direct application of the missing information principle (MIP), the algorithms' performances are proven to range between the conventional supervised and unsupervised MLE extremities proportionally to the information content of the contextual assistance provided. The acquired benefits regard higher estimation precision, smaller standard errors, faster convergence rates and improved classification accuracy or regression fitness shown in various scenarios, while also highlighting important properties and differences among the outlined situations. Applicability is showcased with three real-world unsupervised classification scenarios employing Gaussian Mixture Models. Importantly, we exemplify the natural extension of this methodology to any type of generative model by deriving an equivalent context-aware algorithm for variational autoencoders (VAs), thus broadening the spectrum of applicability to unsupervised deep learning with artificial neural networks. The latter is contrasted with a neural-symbolic algorithm exploiting side-information.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.relation.ispartofIEEE Transactions on Neural Networks and Learning Systemsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectStatisticsde_CH
dc.subjectMachine Learningde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleContext-aware learning for generative modelsde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1109/TNNLS.2020.3011671de_CH
dc.identifier.pmid32776882de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewNot specifiedde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Perdikis, S., Leeb, R., Chavarriaga, R., & Millán, J. d. R. (2020). Context-aware learning for generative models. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2020.3011671
Perdikis, S. et al. (2020) ‘Context-aware learning for generative models’, IEEE Transactions on Neural Networks and Learning Systems [Preprint]. Available at: https://doi.org/10.1109/TNNLS.2020.3011671.
S. Perdikis, R. Leeb, R. Chavarriaga, and J. d. R. Millán, “Context-aware learning for generative models,” IEEE Transactions on Neural Networks and Learning Systems, Aug. 2020, doi: 10.1109/TNNLS.2020.3011671.
PERDIKIS, Serafeim, Robert LEEB, Ricardo CHAVARRIAGA und José del R. MILLÁN, 2020. Context-aware learning for generative models. IEEE Transactions on Neural Networks and Learning Systems [online]. 10 August 2020. DOI 10.1109/TNNLS.2020.3011671. Verfügbar unter: https://repository.essex.ac.uk/28199/
Perdikis, Serafeim, Robert Leeb, Ricardo Chavarriaga, and José del R. Millán. 2020. “Context-Aware Learning for Generative Models.” IEEE Transactions on Neural Networks and Learning Systems, August. https://doi.org/10.1109/TNNLS.2020.3011671.
Perdikis, Serafeim, et al. “Context-Aware Learning for Generative Models.” IEEE Transactions on Neural Networks and Learning Systems, Aug. 2020, https://doi.org/10.1109/TNNLS.2020.3011671.


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