I am a PhD student at Language Technology Institute, Carnegie Mellon University, from fall 2012. I am working with Prof. Abhinav Gupta on joint learning with language and vision and life-long learning.
Previously, I graduated with a bachelor's degree in computer science from Zhejiang University, China. During my undergraduate study, I was mainly under the supervision of Prof. Deng Cai in the State Key Laboratory of CAD & CG. I was a summer intern at UCLA in 2011, mainly work with Prof. Jenn Wortman Vaughan.
My Email address is: enderchen at cs dot cmu dot edu.
Artificial Intelligence, Carnegie Mellon University
Computer Science & Advanced Engineering, Zhejiang University, China
Jiajun Lv, Xinlei Chen, Jin Huang, Hujun Bao. Semi-supervised Mesh Segmentation and Labeling. The 20th Pacific Conference on Computer Graphics and Applications (PG), 2012. [PDF]
Xinlei Chen, Zifei Tong, Haifeng Liu, Deng Cai. Metric Learning with Two-Dimensional Smoothness for Visual Analysis. The 25th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012. [PDF]
Xinquan Qu, Xinlei Chen. Sparse Structured Probabilistic Projections for Factorized Latent Space. The 20th ACM Conference on Information and Knowledge Management (CIKM), 2011. [PDF]
Xinlei Chen, Xinquan Qu, Zijian Li. Image Analysis with Nonlinear Adaptive Dimension Reduction. The 3rd International Conference on Internet Multimedia Computing and Service (ACM ICIMCS), 2011. [PDF]
October 2011 -- November 2011
Worked on learning a distance metric with side information. As the project leader, I quickly learned most state-of-the-art metric learning algorithms and proposed a new algorithm specially designed for images, which achieves a significant improvement.
February 2011--May 2011
Worked on extending the probabilistic canonical correlation analysis (PCCA) to a multi-view learning framework. The generated representation has a property of structured sparsity which enables it to extract both shared and view-dependent information. I participated in both the modeling and the experimentation part.
August 2010--January 2011
Worked on accelerating the spectral clustering algorithm while preserving its high accuracy. Adopting the idea of landmark representation and singular value decomposition (SVD), I proposed a scalable method which has linear time complexity while still preserves good performance.
Interested in playing words in a sentence, in both Chinese and English.