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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

Börje F. Karlsson and Antonio L. Furtado, Conceptual Model and System for Genre-Focused Interactive Storytelling, in ICEC (International Conference on Entertainment Computing), Springer, October 2014

Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen, Knowledge Graph and Text Jointly Embedding, in The 2014 Conference on Empirical Methods on 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

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

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