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[PAST EVENT] Mathematics Colloquium and EXTREEMS-QED Lecture: Graham Taylor (University of Guelph, Canada)
October 30, 2015
2pm - 3pm
Abstract: A central challenge in visual reasoning is that of untangling the many factors of variation that explain an image or video. Photometric and geometric "nuisance" factors are intertwined with the variables of interest, for example, object identity in recognition tasks. To date, the dominant methodology for addressing this challenge has been to engineer a feature extraction pipeline, usually containing multiple stages of processing. An alternative approach is "Representation Learning": relying on the data, instead of feature engineering to learn representations that are invariant to nuisance factors. Techniques that learn multiple layers of representation, which are referred to as "Deep Learning", have demonstrated not only impressive success in recent benchmarks and competitions but applicability to multiple domains. In this talk, I will review the foundations of Deep Learning with an emphasis on computer vision applications. I will also highlight the challenges the field brings to technical computing and the opportunities that may be afforded by parallelization and hardware acceleration. I will also review several open source tools and libraries developed by the community.
Short Bio: Dr. Graham Taylor received his Ph.D. in Computer Science from the University of Toronto in 2009, where he was advised by Geoffrey Hinton and Sam Roweis. He spent two years as a postdoc at the Courant Institute of Mathematical Sciences, New York University working with Chris Bregler, Rob Fergus, and Yann LeCun. In 2012, he joined the School of Engineering at the University of Guelph as an Assistant Professor where he leads the Machine Learning Research Group. His research focuses on statistical machine learning, with an emphasis on deep learning and sequential data. He has applied his research to problems in computer vision, graphics, weather modeling, agriculture, and finance.
Short Bio: Dr. Graham Taylor received his Ph.D. in Computer Science from the University of Toronto in 2009, where he was advised by Geoffrey Hinton and Sam Roweis. He spent two years as a postdoc at the Courant Institute of Mathematical Sciences, New York University working with Chris Bregler, Rob Fergus, and Yann LeCun. In 2012, he joined the School of Engineering at the University of Guelph as an Assistant Professor where he leads the Machine Learning Research Group. His research focuses on statistical machine learning, with an emphasis on deep learning and sequential data. He has applied his research to problems in computer vision, graphics, weather modeling, agriculture, and finance.
Contact
Anh Ninh