报告人: 沈逸飞在读博士, 香港科技大学
时间: 2021年08月31日, 12:00-13:00
地点: 复旦大学邯郸校区化学西楼101会议室
主持人: 朱群喜
腾讯会议: https://meeting.tencent.com/dm/qoS3BBV4MnRK,
会议 ID: 473 3148 4445
摘要：
Compressive sensing and deep learning are the two hottest topic in the past two decades. In this technical sharing, we will cover the basics of compressive sensing and discuss their relationships with the deep learning techniques. Specifically, the following topics will be discussed
-- Motivation: High-dimensional data analysis with low-dimensional model
-- Compressive Sensing: \ell_0 norm, its relaxation \ell_1 norm and the geometry
-- Deep Learning: (Guaranteed) network compression
-- Compressive Sensing: A simple algorithm for \ell_1 norm minimization
-- Deep Learning: Deep Unrolling
-- Compressive Sensing: ODE and optimal algorithm
-- Deep Learning: Neural ODE
Featured publications:
1．Y. Shen, Y. Wu, Y. Zhang, C. Shan, J. Zhang, K. B. Letaief, and D. Li, “How powerful is graph convolution for recommendation?,” ACM International Conference on Information and Knowledge Management (CIKM), virtual conference, Nov. 2021.
2．Y. Shen, Y. Shi, J. Zhang, and K. B. Letaief, “Graph neural networks for scalable radio resource management: architecture design and theoretical analysis,” IEEE J. Select. Areas Commun, vol. 39, no. 1, pp. 101–115, Jan. 2021.
3．Y. Shen, Y. Shi, J. Zhang, and K. B. Letaief, “LORM: Learning to optimize for resource management in wireless networks with few training samples,” IEEE Trans. Wireless Commun., vol. 19, no. 1, pp. 665–679, Jan. 2020.
4．Y. Shen, Y. Xue, J. Zhang, K. B. Letaief, and V. Lau, “Complete Dictionary Learning via 
-norm Maximization,” Conference on Uncertainty in Artificial Intelligence (UAI) 2020, Toronto, Canada, Aug. 2020.