Learning Distributed Representations for Statistical.

A. Mnih and G. Hinton. Three new graphical models for statistical language modelling. Proceedings of the 24th International Conference on Machine Learning, pages 641-648, 2007. Google Scholar; Andriy Mnih and Geoffrey Hinton. A scalable hierarchical distributed language model. In Advances in Neural Information Processing Systems, volume 21, 2009.

Jasper Snoek, Hugo Larochelle, Kevin Swersky, Andriy Mnih, George Dahl, Iain Murray and Ryan Adams. On a personal note I would like to thank my parents who were always there for me and whose encouragement got me through the hardest times. Finally, a special thank you goes to my ancee.

Many thanks goes to the members of the Toronto Machine Learning group: Andriy Mnih, Iain Murray, Vinod Nair, Ilya Sutskever, and Tijmen Tieleman for many interesting and inspiring discussions. I also thank my parents and my family for their continuing support. Finally, a special thank you.

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Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, and Daan Wierstra. Deep AutoRegressive networks. In Proceedings of the 31st International Conference on Machine Learning (ICML), pages 1242-1250, 2014. Google Scholar; Arthur Guez. Sample-Based Search Methods For Bayes-Adaptive Planning. PhD thesis, University College London, 2015.

Machine Learning Approaches to Biological Sequence and Phenotype Data Analysis Renqiang Min Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2010 To understand biology at a system level, I presented novel machine learning algorithms to reveal the underlying mechanisms of how genes and their products function in.

Missing Data Problems in Machine Learning Benjamin M. Marlin Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2008 Learning, inference, and prediction in the presence of missing data are pervasive problems in machine learning and statistical data analysis. This thesis focuses on the problems of collab-.

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The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be prevented.

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