Rongrong Lin (born in 1990) is currently a Research Associate Professor of School of Data and Computer Science, Sun Yat-sen University, Guangzhou. He received Ph.D. in computational mathematics from Sun Yat-sen University, Guangzhou in June 2017. From October 2015 to October 2016, he was a Research Assistant with the Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada.
Kernel Methods in Machine Learning and Time-Frequency Analysis
1. R. Lin, H. Zhang* and J. Zhang, On Reproducing Kernel Banach Spaces: Generic Definition and Unified Framework of Constructions, 25 pages.
2. R. Lin*, An Optimal Convergence Rate for Gaussian Regularized Shannon Sampling Series, arXiv: 1711.04909, 14 pages.
3. R. Lin and H. Zhang*, Convergence Analysis of the Gaussian Regularized Shannon Sampling Series, Numerical Functional Analysis and Optimization, 38 (2017), no. 2,224–247.
4. F. Huang, R. Lin* and Y.Yang, The Circular Bedrosian Identity and Multidimensional Periodic Analytic Signals, Complex Variables and Elliptic Equations, 62(2017), no. 2, 199–213.
5. R. Lin and H. Zhang*, Existence of the Bedrosian Identity for Fourier Multiplier Operators, Forum Mathematicum, 28 (2016), no. 4, 749–759.
6. W. Hu, R. Lin* and H. Zhang, The Circular Bedrosian Identity for Translation-Invariant Operators: Existence and Characterization, Mathematical Methods in the Applied Sciences, 38 (2015), no.18, 5264–5270.
1. Invited talk, 25 mins. The 2nd International Conference on Kernel-based Approximation Methods in Data Analysis. Guangzhou. May 25-27, 2018. (Topic: Reproducing Kernel Banach Spaces)
2. Invited talk, 40 mins. TSMIF Sanya workshop: From Approximation Theory to Real World Applications. TSMIF, Sanya. December 11-15, 2017. (Topic: Shannon's Sampling Theorem)
2017.09-2018.01 Wavelet Analysis (小波分析). Outline of this course is as below.
Lecture 1: Introduction to DCT- and DWT-based JPEG
Lecture 2: Regularity of a Function and Decay of its Fourier Coefficients
Lecture 3: DFT, FFT and DCT
Lecture 4: Fourier transform on L1(R) and L2(R)
Lecture 5: Approximation Identity and Shannon's Sampling Theorem
Lecture 6: Celebrated Results in Fourier Analysis
Lecture 7: Wavelet Analysis: Haar Wavelet
Lecture 8: General Multiresolution Analysis and the Mallat Algorithm
Lecture 9: Filter Banks (Symmetry, Vanishing Moments, Sum Rules and Linear-phase Moments)
Lecture 10: Daubechies' Orthogonal Wavelets
Lecture 11: Biorthogonal Wavelets and Discrete Wavelet Transform
2018.03--2018.06 An Introduction to Deep Learning (深度学习)
Lecture 4: Recurrent Neural Networks (RNN) and LSTM (Speaker: 李悦)
Lecture 7: Generative Adversarial Networks (GAN)
Lecture 8: Sparse Coding and Auto-Encoder (Speaker: 袁淦钊)
Lecture 9: Object Detection (R-CNN, YOLO)
Lecture 10: Kernel Methods for Deep Learning (RBF Network, DKL, VC Dimension)
Lecture 12: Kernel Methods and Deep Networks
Talk For Undergraduates: Deep Learning and Its Application to Image Classfication
Talk For Foshan Meteorological Bureau: Deep Learning and Its Application to Meteorology