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ORCIDZhi-Qin John Xu
Person information
- unicode name: 许志钦
- affiliation: Shanghai Jiao Tong University, School of Mathematical Sciences, Shanghai, China
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2020 – today
- 2025
[j10]Xiong-bin Yan, Zhi-Qin John Xu, Zheng Ma:
Bayesian Inversion with Neural Operator (BINO) for modeling subdiffusion: Forward and inverse problems. J. Comput. Appl. Math. 454: 116191 (2025)- 2024
- [j9]Tianyi Chen, Zhi-Qin John Xu:
Efficient and Flexible Method for Reducing Moderate-Size Deep Neural Networks with Condensation. Entropy 26(7): 567 (2024) - [j8]Zhongwang Zhang
, Zhi-Qin John Xu:
Implicit Regularization of Dropout. IEEE Trans. Pattern Anal. Mach. Intell. 46(6): 4206-4217 (2024) - [c8]Zhongwang Zhang, Yuqing Li, Tao Luo, Zhi-Qin John Xu:
Stochastic Modified Equations and Dynamics of Dropout Algorithm. ICLR 2024 - [i53]Zhi-Qin John Xu, Junjie Yao, Yuxiao Yi, Liangkai Hang, Weinan E, Yaoyu Zhang, Tianhan Zhang:
Solving multiscale dynamical systems by deep learning. CoRR abs/2401.01220 (2024) - [i52]Zhongwang Zhang, Zhiwei Wang, Junjie Yao, Zhangchen Zhou, Xiaolong Li, Weinan E, Zhi-Qin John Xu:
Anchor function: a type of benchmark functions for studying language models. CoRR abs/2401.08309 (2024) - [i51]Hanxu Zhou, Yuan Zhang, Guangjie Leng, Ruofan Wang, Zhi-Qin John Xu:
Understanding Time Series Anomaly State Detection through One-Class Classification. CoRR abs/2402.02007 (2024) - [i50]Tianyi Chen, Zhi-Qin John Xu:
Efficient and Flexible Method for Reducing Moderate-size Deep Neural Networks with Condensation. CoRR abs/2405.01041 (2024) - [i49]Zhiwei Wang, Lulu Zhang, Zhongwang Zhang, Zhi-Qin John Xu:
Loss Jump During Loss Switch in Solving PDEs with Neural Networks. CoRR abs/2405.03095 (2024) - [i48]Zhongwang Zhang, Pengxiao Lin, Zhiwei Wang, Yaoyu Zhang, Zhi-Qin John Xu:
Initialization is Critical to Whether Transformers Fit Composite Functions by Inference or Memorizing. CoRR abs/2405.05409 (2024) - [i47]Zhiwei Wang, Yunji Wang, Zhongwang Zhang, Zhangchen Zhou, Hui Jin, Tianyang Hu, Jiacheng Sun, Zhenguo Li, Yaoyu Zhang, Zhi-Qin John Xu:
Towards Understanding How Transformer Perform Multi-step Reasoning with Matching Operation. CoRR abs/2405.15302 (2024) - [i46]Zhangchen Zhou, Yaoyu Zhang, Zhi-Qin John Xu:
A rationale from frequency perspective for grokking in training neural network. CoRR abs/2405.17479 (2024) - 2023
- [j7]Runze Mao, Minqi Lin, Yan Zhang, Tianhan Zhang, Zhi-Qin John Xu, Zhi X. Chen:
DeepFlame: A deep learning empowered open-source platform for reacting flow simulations. Comput. Phys. Commun. 291: 108842 (2023) - [j6]Xi-An Li, Zhi-Qin John Xu, Lei Zhang:
Subspace decomposition based DNN algorithm for elliptic type multi-scale PDEs. J. Comput. Phys. 488: 112242 (2023) - [j5]Leyang Zhang, Zhi-Qin John Xu, Tao Luo, Yaoyu Zhang:
Limitation of Characterizing Implicit Regularization by Data-independent Functions. Trans. Mach. Learn. Res. 2023 (2023) - [i45]Zhengan Chen, Yuqing Li, Tao Luo, Zhangchen Zhou, Zhi-Qin John Xu:
Phase Diagram of Initial Condensation for Two-layer Neural Networks. CoRR abs/2303.06561 (2023) - [i44]Xiong-bin Yan, Zhi-Qin John Xu, Zheng Ma:
Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of subdiffusion. CoRR abs/2304.00909 (2023) - [i43]Zhangchen Zhou, Hanxu Zhou, Yuqing Li, Zhi-Qin John Xu:
Understanding the Initial Condensation of Convolutional Neural Networks. CoRR abs/2305.09947 (2023) - [i42]Zhongwang Zhang, Zhi-Qin John Xu:
Loss Spike in Training Neural Networks. CoRR abs/2305.12133 (2023) - [i41]Zhongwang Zhang, Yuqing Li, Tao Luo, Zhi-Qin John Xu:
Stochastic Modified Equations and Dynamics of Dropout Algorithm. CoRR abs/2305.15850 (2023) - [i40]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. CoRR abs/2306.13385 (2023) - [i39]Yaoyu Zhang, Zhongwang Zhang, Leyang Zhang, Zhiwei Bai, Tao Luo, Zhi-Qin John Xu:
Optimistic Estimate Uncovers the Potential of Nonlinear Models. CoRR abs/2307.08921 (2023) - [i38]Xiong-bin Yan, Keke Wu, Zhi-Qin John Xu, Zheng Ma:
An Unsupervised Deep Learning Approach for the Wave Equation Inverse Problem. CoRR abs/2311.04531 (2023) - 2022
- [j4]Zhemin Li, Zhi-Qin John Xu, Tao Luo, Hongxia Wang:
A regularised deep matrix factorised model of matrix completion for image restoration. IET Image Process. 16(12): 3212-3224 (2022) - [j3]Tao Luo, Zheng Ma, Zhi-Qin John Xu, Yaoyu Zhang:
On the Exact Computation of Linear Frequency Principle Dynamics and Its Generalization. SIAM J. Math. Data Sci. 4(4): 1272-1292 (2022) - [c7]Tao Luo, Zheng Ma, Zhiwei Wang, Zhiqin John Xu, Yaoyu Zhang:
An Upper Limit of Decaying Rate with Respect to Frequency in Linear Frequency Principle Model. MSML 2022: 205-214 - [c6]Hanxu Zhou, Qixuan Zhou, Zhenyuan Jin, Tao Luo, Yaoyu Zhang, Zhi-Qin John Xu:
Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width. NeurIPS 2022 - [c5]Hanxu Zhou, Qixuan Zhou, Tao Luo, Yaoyu Zhang, Zhi-Qin John Xu:
Towards Understanding the Condensation of Neural Networks at Initial Training. NeurIPS 2022 - [e1]Bin Dong, Qianxiao Li, Lei Wang, Zhi-Qin John Xu:
Mathematical and Scientific Machine Learning, 15-17 August 2022, Peking University, Beijing, China. Proceedings of Machine Learning Research 190, PMLR 2022 [contents] - [i37]Zhiwei Wang, Yaoyu Zhang, Yiguang Ju, Weinan E, Zhi-Qin John Xu, Tianhan Zhang:
A deep learning-based model reduction (DeePMR) method for simplifying chemical kinetics. CoRR abs/2201.02025 (2022) - [i36]Tianhan Zhang, Yuxiao Yi, Yifan Xu, Zhi X. Chen, Yaoyu Zhang, Weinan E, Zhi-Qin John Xu:
A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kinetics. CoRR abs/2201.03549 (2022) - [i35]Zhi-Qin John Xu, Yaoyu Zhang, Tao Luo:
Overview frequency principle/spectral bias in deep learning. CoRR abs/2201.07395 (2022) - [i34]Leyang Zhang, Zhi-Qin John Xu, Tao Luo, Yaoyu Zhang:
Limitation of characterizing implicit regularization by data-independent functions. CoRR abs/2201.12198 (2022) - [i33]Hanxu Zhou, Qixuan Zhou, Zhenyuan Jin, Tao Luo, Yaoyu Zhang, Zhi-Qin John Xu:
Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width. CoRR abs/2205.12101 (2022) - [i32]Shuyu Yin, Tao Luo, Peilin Liu, Zhi-Qin John Xu:
An Experimental Comparison Between Temporal Difference and Residual Gradient with Neural Network Approximation. CoRR abs/2205.12770 (2022) - [i31]Zhiwei Bai, Tao Luo, Zhi-Qin John Xu, Yaoyu Zhang:
Embedding Principle in Depth for the Loss Landscape Analysis of Deep Neural Networks. CoRR abs/2205.13283 (2022) - [i30]Zhongwang Zhang, Zhi-Qin John Xu:
Implicit regularization of dropout. CoRR abs/2207.05952 (2022) - [i29]Yaoyu Zhang, Zhongwang Zhang, Leyang Zhang, Zhiwei Bai, Tao Luo, Zhi-Qin John Xu:
Linear Stability Hypothesis and Rank Stratification for Nonlinear Models. CoRR abs/2211.11623 (2022) - [i28]Xiong-bin Yan, Zhi-Qin John Xu, Zheng Ma:
Bayesian Inversion with Neural Operator (BINO) for Modeling Subdiffusion: Forward and Inverse Problems. CoRR abs/2211.11981 (2022) - 2021
- [j2]Tao Luo, Zhi-Qin John Xu, Zheng Ma, Yaoyu Zhang:
Phase Diagram for Two-layer ReLU Neural Networks at Infinite-width Limit. J. Mach. Learn. Res. 22: 71:1-71:47 (2021) - [c4]Zhiqin John Xu, Hanxu Zhou:
Deep Frequency Principle Towards Understanding Why Deeper Learning Is Faster. AAAI 2021: 10541-10550 - [c3]Yaoyu Zhang, Zhongwang Zhang, Tao Luo, Zhi-Qin John Xu:
Embedding Principle of Loss Landscape of Deep Neural Networks. NeurIPS 2021: 14848-14859 - [i27]Yuheng Ma, Zhi-Qin John Xu, Jiwei Zhang:
Frequency Principle in Deep Learning Beyond Gradient-descent-based Training. CoRR abs/2101.00747 (2021) - [i26]Yaoyu Zhang, Tao Luo, Zheng Ma, Zhi-Qin John Xu:
Linear Frequency Principle Model to Understand the Absence of Overfitting in Neural Networks. CoRR abs/2102.00200 (2021) - [i25]Tao Luo, Zheng Ma, Zhiwei Wang, Zhi-Qin John Xu, Yaoyu Zhang:
An Upper Limit of Decaying Rate with Respect to Frequency in Deep Neural Network. CoRR abs/2105.11675 (2021) - [i24]Zhi-Qin John Xu, Hanxu Zhou, Tao Luo, Yaoyu Zhang:
Towards Understanding the Condensation of Two-layer Neural Networks at Initial Training. CoRR abs/2105.11686 (2021) - [i23]Yaoyu Zhang, Zhongwang Zhang, Tao Luo, Zhi-Qin John Xu:
Embedding Principle of Loss Landscape of Deep Neural Networks. CoRR abs/2105.14573 (2021) - [i22]Lulu Zhang, Tao Luo, Yaoyu Zhang, Zhi-Qin John Xu, Zheng Ma:
MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs. CoRR abs/2107.03673 (2021) - [i21]Guangjie Leng, Yekun Zhu, Zhi-Qin John Xu:
Force-in-domain GAN inversion. CoRR abs/2107.06050 (2021) - [i20]Lulu Zhang, Zhi-Qin John Xu, Yaoyu Zhang:
Data-informed Deep Optimization. CoRR abs/2107.08166 (2021) - [i19]Zhongwang Zhang, Hanxu Zhou, Zhi-Qin John Xu:
A variance principle explains why dropout finds flatter minima. CoRR abs/2111.01022 (2021) - [i18]Yaoyu Zhang, Yuqing Li, Zhongwang Zhang, Tao Luo, Zhi-Qin John Xu:
Embedding Principle: a hierarchical structure of loss landscape of deep neural networks. CoRR abs/2111.15527 (2021) - [i17]Xi-An Li, Zhi-Qin John Xu, Lei Zhang:
Subspace Decomposition based DNN algorithm for elliptic type multi-scale PDEs. CoRR abs/2112.06660 (2021) - 2020
- [c2]Yaoyu Zhang, Zhi-Qin John Xu, Tao Luo, Zheng Ma:
A type of generalization error induced by initialization in deep neural networks. MSML 2020: 144-164 - [i16]Jihong Wang, Zhi-Qin John Xu, Jiwei Zhang, Yaoyu Zhang:
Implicit bias with Ritz-Galerkin method in understanding deep learning for solving PDEs. CoRR abs/2002.07989 (2020) - [i15]Tao Luo, Zhi-Qin John Xu, Zheng Ma, Yaoyu Zhang:
Phase diagram for two-layer ReLU neural networks at infinite-width limit. CoRR abs/2007.07497 (2020) - [i14]Ziqi Liu, Wei Cai, Zhi-Qin John Xu:
Multi-scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains. CoRR abs/2007.11207 (2020) - [i13]Zhi-Qin John Xu, Hanxu Zhou:
Deep frequency principle towards understanding why deeper learning is faster. CoRR abs/2007.14313 (2020) - [i12]Zhemin Li, Zhi-Qin John Xu, Tao Luo, Hongxia Wang:
A regularized deep matrix factorized model of matrix completion for image restoration. CoRR abs/2007.14581 (2020) - [i11]Tao Luo, Zheng Ma, Zhi-Qin John Xu, Yaoyu Zhang:
On the exact computation of linear frequency principle dynamics and its generalization. CoRR abs/2010.08153 (2020) - [i10]Tao Luo, Zheng Ma, Zhiwei Wang, Zhi-Qin John Xu, Yaoyu Zhang:
Fourier-domain Variational Formulation and Its Well-posedness for Supervised Learning. CoRR abs/2012.03238 (2020)
2010 – 2019
- 2019
- [j1]Zhi-Qin John Xu, Douglas Zhou, David Cai:
Dynamical and Coupling Structure of Pulse-Coupled Networks in Maximum Entropy Analysis. Entropy 21(1): 76 (2019) - [c1]Zhi-Qin John Xu, Yaoyu Zhang, Yanyang Xiao:
Training Behavior of Deep Neural Network in Frequency Domain. ICONIP (1) 2019: 264-274 - [i9]Zhi-Qin John Xu, Yaoyu Zhang, Tao Luo, Yanyang Xiao, Zheng Ma:
Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks. CoRR abs/1901.06523 (2019) - [i8]Yaoyu Zhang, Zhi-Qin John Xu, Tao Luo, Zheng Ma:
A type of generalization error induced by initialization in deep neural networks. CoRR abs/1905.07777 (2019) - [i7]Yaoyu Zhang, Zhi-Qin John Xu, Tao Luo, Zheng Ma:
Explicitizing an Implicit Bias of the Frequency Principle in Two-layer Neural Networks. CoRR abs/1905.10264 (2019) - [i6]Tao Luo, Zheng Ma, Zhi-Qin John Xu, Yaoyu Zhang:
Theory of the Frequency Principle for General Deep Neural Networks. CoRR abs/1906.09235 (2019) - [i5]Wei Cai, Zhi-Qin John Xu:
Multi-scale Deep Neural Networks for Solving High Dimensional PDEs. CoRR abs/1910.11710 (2019) - 2018
- [i4]Zhi-Qin John Xu, Yaoyu Zhang, Yanyang Xiao:
Training behavior of deep neural network in frequency domain. CoRR abs/1807.01251 (2018) - [i3]Zhiqin John Xu:
Understanding training and generalization in deep learning by Fourier analysis. CoRR abs/1808.04295 (2018) - [i2]Zhi-Qin John Xu, Jennifer Crodelle, Douglas Zhou, David Cai:
Maximum Entropy Principle Analysis in Network Systems with Short-time Recordings. CoRR abs/1808.10506 (2018) - [i1]Zhi-Qin John Xu:
Frequency Principle in Deep Learning with General Loss Functions and Its Potential Application. CoRR abs/1811.10146 (2018)
Coauthor Index
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