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Machine Learning and Artificial Intelligence

Automated reasoning and the applications of decision making

We pursue research on automated reasoning, adaptation, and the theories and applications of decision making and learning. Our research goals include learning from data and data mining. By building software that automatically learns from data, we design applications that have new functions and flexibility. Our research focuses on using statistical methods for the development of more advanced, intelligent computer systems.



James D. McCaffrey, Neural Networks with Simplex Optimization, in Visual Studio Magazine, 16 October 2014

Li Deng and Dong Yu, Chapter 13: Recurrent Neural Networks and Related Models, in Automatic Speech Recognition --- A Deep Learning Approach, Springer, October 2014

Kai-Wei Chang, Wen-tau Yih, Bishan Yang, and Christopher Meek, Typed Tensor Decomposition of Knowledge Bases for Relation Extraction, in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, ACL – Association for Computational Linguistics, October 2014

Jianfeng Gao, Patrick Pantel, Michael Gamon, Xiaodong He, Li Deng, and Yelong Shen, Modeling Interestingness with Deep Neural Networks, EMNLP, October 2014

Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang, Urban Computing: Concepts, Methodologies, and Applications, in ACM Transaction on Intelligent Systems and Technology, ACM, October 2014

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