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‘dynamic graphs’. Although recently many studies on extending deep learning approaches for graph data have emerged, there is still a research gap on extending deep learning approaches for identifying
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. Recent works on knowledge graph question generation [4,5] have mainly focussed on multi-hop questions. This project aims at developing novel methods that jointly address the challenging, dual problem
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, Hawaii, U.S.A. P. J. Tan and D. L. Dowe (2003). MML Inference of Decision Graphs with Multi-Way Joins and Dynamic Attributes , Proc. 16th Australian Joint Conference on Artificial Intelligence (AI'03
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. on Statistics, Mathematics and Related Fields, 22 pages, 16th - 18th January, 2006, Hawaii, U.S.A. P. J. Tan and D. L. Dowe (2003). MML Inference of Decision Graphs with Multi-Way Joins and Dynamic Attributes
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on Artificial Intelligence and Statistics (AI+STATS 2001), pp253-260, Key West, Florida, U.S.A., Jan. 2001 P. J. Tan and D. L. Dowe (2003). MML Inference of Decision Graphs with Multi-Way Joins and Dynamic
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: 04EX783), pp439-444 Frey and Osborne (2013) Frey and Osborne (2017) P. J. Tan and D. L. Dowe (2003). MML Inference of Decision Graphs with Multi-Way Joins and Dynamic Attributes , Proc. 16th Australian
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). "Statistical field theory applied to complex networks” "Quantum geometrogenesis – Graph theoretic approaches to building spacetime” web page For further details or to discuss alternative project arrangements
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programming. An ability to code in C, C++ or Rust is also necessary. Candidates with experience or interest in column generation, cutting planes, polyhedral geometry and graph theory are especially invited
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structured domains such as trees and graphs is a challenging but important problem. This project aims to solve these limitations. Novel Adversarial Machine Learning algorithms for structured data will be
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Aim/outline Graphs or networks are effective tools to representing a variety of data in different domains. In the biological domain, chemical compounds can be represented as networks, with atoms as