Regularized Deep Learning with Geometry and Structures-北航国际交流合作处

Regularized Deep Learning with Geometry and Structures

点击[] 时间[ 2018年05月16日 17:24] 发布人[]

主讲人:Qiang Qiu, Ph.D. Assistant Professor (Electrical and Computer Engineering Department Duke University)
报告时间:2018年5月17日 14:00 – 16:00
报告地点:新主楼 会议中心 第一报告厅

The central problem of deep learning is how to generalize well from training data to unseen data. One such solution is to regularize deep learning with priors encoded into models. In this talk, the speaker will discuss various techniques recently developed in regularizing deep learning with geometry, such as low-rank subspace and low-dimensional manifold, or structures over convolutional filters, nodes, and networks. The speaker will present numerous applications in cross-spectral face recognition, image hashing, object recognition, object localization, person re-identification, and privacy preservation.

Dr. Qiu received his Bachelor's degree with first class honors in Computer Science in 2001, and his Master's degree in Computer Science in 2002, from National University of Singapore. He received his Ph.D. degree in Computer Science in 2013 from University of Maryland, College Park. During 2002-2007, he was a Senior Research Engineer at Institute for Infocomm Research, Singapore. He is currently with the Department of Electrical and Computer Engineering, Duke University. His research interests include machine learning, computer vision, and pattern recognition with applications in biometrics and imaging.

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