Yujia Li |
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 Research Scientist DeepMind 
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I'm a Research Scientist at DeepMind. I obtained my Ph.D. from University of Toronto in 2017. I was a member of the
machine learning group and my advisor was Prof.
Richard Zemel. Before coming to Toronto, I obtained my bachelor's degree from
Tsinghua University, Beijing, China. I was an
intern Research and Development engineer at Baidu, Inc. in the summer of 2011, a Research Intern with the
Speech Group at Microsoft Research Redmond in the summer of 2014,
and a Research Intern with the Machine Learning and Perception group at Microsoft Research Cambridge in the summer of 2015.News
Education
PublicationsPreprintsLearning Deep Generative Models of GraphsYujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia arXiv:1803.03324, Invited to ICLR Workshop Track, 2018 [arXiv] Learning Model-Based Planning from Scratch Razvan Pascanu*, Yujia Li*, Oriol Vinyals, Nicolas Heess, Lars Buesing, Sébastien Racanière, David Reichert, Théophane Weber, Daan Wierstra, Peter Battaglia (* denotes equal contribution) arXiv:1707.06170 [arXiv] Conference PapersDualing GANsYujia Li, Alexander Schwing, Kuan-Chieh Wang and Richard Zemel Neural Information Processing Systems (NIPS), 2017 Spotlight Presentation [arXiv] [poster] [teaser slides] Imagination-Augmented Agents for Deep Reinforcement Learning Théophane Weber*, Sébastien Racanière*, David P. Reichert*, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adria Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter Battaglia, David Silver, Daan Wierstra (* denotes equal contribution) Neural Information Processing Systems (NIPS), 2017 Oral Presentation [arXiv] Understanding the Effective Receptive Field in Deep Convolutional Neural Networks Wenjie Luo*, Yujia Li*, Raquel Urtasun, and Richard Zemel (* denotes equal contribution) Neural Information Processing Systems (NIPS), 2016 [paper] [poster] Gated Graph Sequence Neural Networks Yujia Li, Daniel Tarlow, Marc Brockschmidt and Richard Zemel International Conference on Learning Representations (ICLR), 2016 [arXiv] [slides] [poster] [code] [talk] The Variational Fair Auto Encoder Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard Zemel International Conference on Learning Representations (ICLR), 2016 Oral Presentation [arXiv] Generative Moment Matching Networks Yujia Li, Kevin Swersky and Richard Zemel International Conference on Machine Learning (ICML), 2015 [paper] [arXiv preprint] [code] [project page] Feedback-Based Handwriting Recognition from Inertial Sensor Data for Wearable Devices Yujia Li, Kaisheng Yao and Geoffrey Zweig The 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015 [paper] High Order Regularization for Semi-Supervised Learning of Structured Output Problems Yujia Li and Richard Zemel International Conference on Machine Learning (ICML), 2014 [paper + supplementary material] [slides] [poster] [data] [code] [video] Exploring Compositional High Order Pattern Potentials for Structured Output Learning Yujia Li, Daniel Tarlow and Richard Zemel The 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 Oral Presentation [paper] [supplementary material] [slides] [poster] [data] [code] [video] Celebrity Recommendation with Collaborative Social Topic Regression Xuetao Ding, Xiaoming Jin, Yujia Li and Lianghao Li Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013 [paper] Workshop PapersLearning Unbiased FeaturesYujia Li, Kevin Swersky and Richard Zemel NIPS workshop on Transfer and Multi-Task Learning, 2014 [paper] [slides] [poster] Mean Field Networks Yujia Li and Richard Zemel ICML workshop on Learning Tractable Probabilistic Models, 2014 [paper] [slides] [poster] ThesisBuilding More Expressive Structured ModelsPh.D. Thesis [paper] Exploring Compositional High Order Pattern Potentials for Structured Output Learning M.Sc. Thesis [paper] TalksGenerative Moment Matching Networks (2015) at Toronto Machine Learning Group Seminar. [pdf] A Tutorial on Dual Decomposition (2014) at Toronto Machine Learning Group Meeting. [pdf]Teaching ExperienceCourses TA'ed at University of Toronto
Honors and Awards
Other StuffMy name in Chinese: A list of my old projects can be found here. I swim and run regularly every week. I like playing badminton. I'm a fan of NBA. Following are some activities that I have participated in.
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