Xiangyu Meng

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Xiangyu Meng,
Postgraduate,
DUT MEDIA LAB,
DUT-RU International School of Information Science & Engineering,
School of Software Technology,
Dalian University of Technology,
Dalian, China
E-post: lnmengxiangyu [@] mail [DOT] dlut [DOT] edu [DOT] cn
E-post: lnmengxiangyu [@] gmail [DOT] com

About me

I am a Master Student with DUT-RU International School of Information Science & Engineering at Dalian University of Technology, Dalian, China.
I received a B.Eng. degree with Honours in Cyber Engineering from the School of Software Technology, Dalian University of Technology (DUT), Dalian, China in 2020.

Research

My research interests include

  • Computer Vision

  • Image Processing

  • Optimization

  • Statistical Learning

Find out more.

Recent Publications

  • Xinwei Xue, Xiangyu Meng, Long Ma, Risheng Liu, Xin Fan, "GTA-Net: Gradual Temporal Aggregation Network for Fast Video Deraining", In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021.

  • 孟祥玉, 薛昕惟, 李汶霖, 王祎, "基于运动估计与时空结合的多帧融合去雨网络", 计算机科学, 2021-5

Full list of publications.

Recommended Optimization Textbooks

  • Convex Optimization, by S. Boyd and L. Vandenberghe, Cambridge University Press, 2003.

  • Introduction to Nonlinear Optimization: Theory, Algorithms, and Applications with MATLAB, by A. Beck, MOS-SIAM Series on Optimization, 2014.

  • Optimization Models, by G.C. Calafiore and L. El Ghaoui, Cambridge University Press, 2014.

  • Convex Optimization for Signal Processing and Communications From Fundamentals to Applications, by Chong-Yung Chi, Wei-Chiang Li, Chia-Hsiang Lin, CRC Press, 2017.

  • Optimization: Modeling, Algorithms and Theory (最优化:建模、算法与理论), by H. Liu, J. Hu, Y. Li, Z. Wen, Higher Education Press (高等教育出版社), 2020.

  • First-order Methods in Optimization, by A. Beck, MOS-SIAM Series on Optimization, 2017.

  • Foundations of Bilevel Programming, by Stephan Dempe, Springer, 2002.

  • High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications, by John Wright and Yi Ma, Cambridge University Press, 2020.

  • Accelerated Optimization for Machine Learning First-Order Algorithms, by Zhouchen Lin, Huan Li and Cong Fang, Springer, 2020.

  • High-Dimensional Probability: An Introduction with Applications in Data Science, by Roman Vershynin, Cambridge University Press, 2018.

  • High-Dimensional Statistics: A Non-Asymptotic Viewpoint, by Martin Wainwright, Cambridge University Press, 2019.

  • Numerical Optimization 2nd Edition, by Jorge Nocedal and Stephen J. Wright, Springer, 2006.

  • Convex Optimization Algorithms, by Dimitri P. Bertsekas, Athena Scientific, 2015.

  • Nonlinear Programming: 3rd Edition, by Dimitri P. Bertsekas, Athena Scientific, 2016.

  • Lectures on Convex Optimization, by Yurii Nesterov, Springer, 2018.

  • Non-convex Optimization for Machine Learning, by P. Jain and P. Kark, Foundations and Trends in Machine Learning, 2017.