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Thesis lab srl

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Statistical Relational Learning SRL , studies techniques that combine the strengths of relational learning e. Bayesian networks. By combining the power of logic and probability, such systems can perform robust and accurate reasoning and learning about complex relational data. See the book: Introduction to Statistical Relational Learning. Our work in the area has primarily focused on applications of SRL methods to problems in natural language processing , transfer learning , and abductive reasoning. Beltagy and Raymond J.

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The data contained in this document was collected during the characterization study of these basins. This study involved the analysis of sediment samples from the bottom of each basin and the analysis of groundwater samples collected from nine surrounding monitoring wells and one remote well. Data interpretations, as they pertain to the closure of the basins, can be found in a separate site assessment report. The basins are tentatively scheduled to be closed by the end of Similar records in OSTI.
Virtual Defense via Zoom. This dissertation presents digital diagnosis tools by adapting existing neuropsychological tests and fully automating what is otherwise a subjective process requiring domain expertise. We present the first fully-automated Rey-Osterrieth Complex Figure grader that can recognize all 18 grading details using a series of agent-based graph traversal algorithms combined with a modified template- matching gesture recognition model. We also present among the first systems to recognize MCI on digitized Trail-Making tests combining machine learning methods with digital sketch recognition. He has studied the effects of cognitive decline on touch tablets, stylus input, digitized paper-and-pencil sketching, and tests integrating augmented reality.

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