Zhi-Qin John Xu
Orcid: 0000-0003-0627-3520Affiliations:
- Shanghai Jiao Tong University, School of Mathematical Sciences, Shanghai, China
According to our database1,
Zhi-Qin John Xu
authored at least 58 papers
between 2018 and 2025.
Collaborative distances:
Collaborative distances:
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Bibliography
2025
Bayesian Inversion with Neural Operator (BINO) for modeling subdiffusion: Forward and inverse problems.
J. Comput. Appl. Math., 2025
2024
Efficient and Flexible Method for Reducing Moderate-Size Deep Neural Networks with Condensation.
Entropy, July, 2024
IEEE Trans. Pattern Anal. Mach. Intell., June, 2024
CoRR, 2024
Towards Understanding How Transformer Perform Multi-step Reasoning with Matching Operation.
CoRR, 2024
Initialization is Critical to Whether Transformers Fit Composite Functions by Inference or Memorizing.
CoRR, 2024
CoRR, 2024
CoRR, 2024
Proceedings of the Twelfth International Conference on Learning Representations, 2024
2023
<i>DeepFlame</i>: A deep learning empowered open-source platform for reacting flow simulations.
Comput. Phys. Commun., October, 2023
J. Comput. Phys., September, 2023
Trans. Mach. Learn. Res., 2023
CoRR, 2023
Solving a class of multi-scale elliptic PDEs by means of Fourier-based mixed physics informed neural networks.
CoRR, 2023
Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of subdiffusion.
CoRR, 2023
2022
On the Exact Computation of Linear Frequency Principle Dynamics and Its Generalization.
SIAM J. Math. Data Sci., 2022
A regularised deep matrix factorised model of matrix completion for image restoration.
IET Image Process., 2022
Embedding Principle in Depth for the Loss Landscape Analysis of Deep Neural Networks.
CoRR, 2022
An Experimental Comparison Between Temporal Difference and Residual Gradient with Neural Network Approximation.
CoRR, 2022
A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kinetics.
CoRR, 2022
A deep learning-based model reduction (DeePMR) method for simplifying chemical kinetics.
CoRR, 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
An Upper Limit of Decaying Rate with Respect to Frequency in Linear Frequency Principle Model.
Proceedings of the Mathematical and Scientific Machine Learning, 2022
2021
J. Mach. Learn. Res., 2021
Embedding Principle: a hierarchical structure of loss landscape of deep neural networks.
CoRR, 2021
MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs.
CoRR, 2021
Towards Understanding the Condensation of Two-layer Neural Networks at Initial Training.
CoRR, 2021
CoRR, 2021
Linear Frequency Principle Model to Understand the Absence of Overfitting in Neural Networks.
CoRR, 2021
CoRR, 2021
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021
2020
Fourier-domain Variational Formulation and Its Well-posedness for Supervised Learning.
CoRR, 2020
A regularized deep matrix factorized model of matrix completion for image restoration.
CoRR, 2020
Multi-scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains.
CoRR, 2020
Implicit bias with Ritz-Galerkin method in understanding deep learning for solving PDEs.
CoRR, 2020
Proceedings of Mathematical and Scientific Machine Learning, 2020
2019
Dynamical and Coupling Structure of Pulse-Coupled Networks in Maximum Entropy Analysis.
Entropy, 2019
Explicitizing an Implicit Bias of the Frequency Principle in Two-layer Neural Networks.
CoRR, 2019
CoRR, 2019
Proceedings of the Neural Information Processing - 26th International Conference, 2019
2018
Frequency Principle in Deep Learning with General Loss Functions and Its Potential Application.
CoRR, 2018
CoRR, 2018
CoRR, 2018