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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.

Videos

Publications

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

Dong Yu and Li Deng, Automatic Speech Recognition - A Deep Learning Approach, 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

Daniel Khashabi, Sebastian Nowozin, Jeremy Jancsary, and Andrew Fitzgibbon, Joint Demosaicing and Denoising via Learned Non-parametric Random Fields, in Transactions on Image Processing, IEEE – Institute of Electrical and Electronics Engineers, 1 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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