Yiping Lu

Undergraduate

Department of Scientific & Engineering Computing
School of mathematical sciences
Peking University
The elite undergraduate training program of School of Mathematical Sciences in Peking University.(Both applied math and pure math track)

Email: luyiping9712 at pku dot edu dot cn
Contact: No. 5 Yiheyuan Road Beijing, 100871 People's Republic of China


Publications List

Research Areas:

      
Zichao Long, Yiping Lu, Bin Dong. " PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network" Submitted.

[ paper] [ arXiv] [code] [ slide] [ project page]

Ting Lin*, Yiping Lu*, Bin Dong, Zuowei Shen. " A New Edge Driven Wavelet Frame Image Restoration Model: The Mumford--Shah functional, Unnatural Zero Norm Minimization And Beyond" In Preparation(*equal contribution)

[ paper] [ arXiv] [code] [ slide] [ project page]

In this paper, we give an asymptotic analysis to a class of modified $ell_0$ models and gives a new image reconstruction algorithm joint reconstruction and jump set estimation. We give a geometry view to the "true" sparsity models.
Xiaoshuai Zhang*, Yiping Lu*, Jiaying Liu, Bin Dong. "Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration" preprint(*equal contribution)

[ paper] [ arXiv] [code] [ slide] [ project page]

In this paper, we propose a new control framework called the moving endpoint control to restore images corrupted by different degradation levels in one model. The proposed control problem contains a restoration dynamics which is modeled by an RNN. The moving endpoint, which is essentially the terminal time of the associated dynamics, is determined by a policy network. We call the proposed model the dynamically unfolding recurrent restorer (DURR). Numerical experiments show that DURR is able to achieve state-of-the-art performances on blind image denoising and JPEG image deblocking. Furthermore, DURR can well generalize to images with higher degradation levels that are not included in the training stage.
Yiping Lu, Aoxiao Zhong, Quanzheng Li, Bin Dong. "Beyond Finite Layer Neural Network:Bridging Deep Architects and Numerical Differential Equations" Thirty-fifth International Conference on Machine Learning (ICML), 2018

[ paper] [ arXiv] [ project page] [ slide][ bibtex][ Poster]

This work bridge deep neural network design with numerical differential equations. We show that many effective networks can be interpreted as different numerical discretizations of differential equations. This finding brings us a brand new perspective on the design of effective deep architectures. We can take advantage of the rich knowledge in numerical analysis to guide us in designing new and potentially more effective deep networks. As an example, we propose a linear multi-step architecture (LM-architecture) which is inspired by the linear multi-step method solving ordinary differential equations.
Zichao long*, Yiping Lu*, Xianzhong Ma*, Bin Dong. "PDE-Net:Learning PDEs From Data", Thirty-fifth International Conference on Machine Learning (ICML), 2018(*equal contribution)

[ paper] [ arXiv] [ code] [ Supplementary Materials][ bibtex]

This paper is an initial attempt to learn evolution PDEs from data. Inspired by the latest development of neural network designs in deep learning, we propose a new feed-forward deep network, called PDE-Net, to fulfill two objectives at the same time: to accurately predict dynamics of complex systems and to uncover the underlying hidden PDE models. The basic idea of the proposed PDE-Net is to learn differential operators by learning convolution kernels (filters), and apply neural networks or other machine learning methods to approximate the unknown nonlinear responses.

Other Interesting Results

Mathematical Olympiad Problem Providing

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Mathematical Olympiad Problem. High-School Mathematics(2012.3,ISSN:1005-6416)[Pdf]

A famous magazine in China about mathematical Olympiad.



© Yiping Lu | Last updated: 11/08/2018

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Theory without practice is empty, but equally, practice without theory is blind. ---- I. Kant

People who wish to analyze nature without using mathematics must settle for a reduced understanding. ---- Richard Feynman