Connecting Deep Neural Networks with Symbolic Knowledge

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dc.contributor.advisorOates, Tim
dc.contributor.authorKumar, Arjun
dc.contributor.departmentComputer Science and Electrical Engineering
dc.contributor.programComputer Science
dc.date.accessioned2019-10-11T13:42:48Z
dc.date.available2019-10-11T13:42:48Z
dc.date.issued2016-01-01
dc.description.abstractNeural networks have attracted significant interest in recent years due to their exceptional performance in various domains ranging from natural language processing to image identification and classification. Modern deep neural networks demonstrate state-of-the-art results in complex tasks such as epileptic seizure detection and time series classification. The internal architecture of these networks, in terms of learned representations, still remains opaque. This research addresses the first step in the long term motivation to construct a bi-directional connection between the raw input data and their symbolic representations. In this research, we examined whether a denoising autoencoder can internally find correlated principal features from input images and their symbolic representations which can be used to generate one from the other. Our results indicate that using symbolic representations along with the raw inputs generates better reconstructions. Our network was able to construct the symbolic representations from the input as well as input instances from their symbolic representations.
dc.genretheses
dc.identifierdoi:10.13016/m2pqdf-yvgo
dc.identifier.other11544
dc.identifier.urihttp://hdl.handle.net/11603/15481
dc.languageen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Theses and Dissertations Collection
dc.relation.ispartofUMBC Graduate School Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
dc.sourceOriginal File Name: Kumar_umbc_0434M_11544.pdf
dc.subjectAutoencoder
dc.subjectDeep Neural Networks
dc.subjectMachine Learning
dc.subjectSymbolic Knowledge
dc.titleConnecting Deep Neural Networks with Symbolic Knowledge
dc.typeText
dcterms.accessRightsDistribution Rights granted to UMBC by the author.

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