Context-Aware Learning for Generative Models Article Swipe
Serafeim Perdikis
,
Robert Leeb
,
Ricardo Chavarriaga
,
José del R. Millán
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/tnnls.2020.3011671
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/tnnls.2020.3011671
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 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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tnnls.2020.3011671
- OA Status
- green
- Cited By
- 8
- References
- 92
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3048633995
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3048633995Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tnnls.2020.3011671Digital Object Identifier
- Title
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Context-Aware Learning for Generative ModelsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-08-10Full publication date if available
- Authors
-
Serafeim Perdikis, Robert Leeb, Ricardo Chavarriaga, José del R. MillánList of authors in order
- Landing page
-
https://doi.org/10.1109/tnnls.2020.3011671Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1507.08272Direct OA link when available
- Concepts
-
Generative grammar, Context (archaeology), Generative model, Computer science, Psychology, Artificial intelligence, Geography, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 4, 2022: 2, 2021: 1, 2020: 1Per-year citation counts (last 5 years)
- References (count)
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92Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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