2025年3月11日 · Zhi-Qin John Xu is an associate professor at the Institute of Natural Sciences/School of Mathematical Sciences, Shanghai Jiao Tong University. Zhi-Qin graduated from Zhiyuan College of...

Neural Information Processing: 26th International Conference, ICONIP 2019 …

Zhi-Qin John Xu*, Yaoyu Zhang, and Yanyang Xiao, Training behavior of deep neural network in frequency domain, arXiv preprint: 1807.01251, (2018), 26th International Conference on Neural...

2016 Ph.D. in Mathematics, School of Mathematical Sciences, Shanghai Jiao Tong University, China. 2012 B.S. in Physics (major) and Mathematics (minor), Zhiyuan College, Shanghai Jiao Tong...

2019年1月19日 · We demonstrate a very universal Frequency Principle (F-Principle) -- DNNs often fit target functions from low to high frequencies -- on high-dimensional benchmark datasets such as MNIST/CIFAR10 and deep neural networks such as VGG16.

Zhi-Qin graduated from Zhiyuan College of Shanghai Jiao Tong University in 2012. In 2016, he graduated from Shanghai Jiao Tong University with a doctor's degree in applied mathematics. From 2016 to 2019, he was a postdoctoral fellow at NYU ABU Dhabi and the Courant Institute.

2020年7月28日 · View a PDF of the paper titled Deep frequency principle towards understanding why deeper learning is faster, by Zhi-Qin John Xu and 1 other authors

This code repository comprises a series of fundamental Deep Neural Network (DNN) experiments designed to assist you in comprehending and mastering the basic concepts of deep learning. Additionally, we have also verified two crucial phenomena: the Frequency Principle and the Condensation Phenomenon.

Zhi-Qin John Xu received the graduated degree from the Zhiyuan College of Shanghai Jiao Tong University, in 2012, and the doctor's degree in applied mathematics from Shanghai Jiao Tong University, in 2016. He is an Associate Professor with the Institute of Natural Sciences/School of Mathematical Sciences, Shanghai Jiao Tong University.

2025年3月13日 · Xi-An Li, Jinran Wu, Zhi-Qin John Xu, You-Gan Wang: Solving a class of multi-scale elliptic PDEs by means of Fourier-based mixed physics informed neural networks.