| Frequency principle: Fourier analysis sheds light on deep neural networks ZQJ Xu, Y Zhang, T Luo, Y Xiao, Z Ma arXiv preprint arXiv:1901.06523, 2019 | 608 | 2019 |
| Training behavior of deep neural network in frequency domain ZQJ Xu, Y Zhang, Y Xiao Neural Information Processing: 26th International Conference, ICONIP 2019 …, 2019 | 341 | 2019 |
| Theory of the frequency principle for general deep neural networks T Luo, Z Ma, ZQJ Xu, Y Zhang arXiv preprint arXiv:1906.09235, 2019 | 86 | 2019 |
| Overview frequency principle/spectral bias in deep learning ZQJ Xu, Y Zhang, T Luo Communications on Applied Mathematics and Computation, 1-38, 2024 | 79 | 2024 |
| Phase diagram for two-layer relu neural networks at infinite-width limit T Luo, ZQJ Xu, Z Ma, Y Zhang Journal of Machine Learning Research 22 (71), 1-47, 2021 | 76 | 2021 |
| A type of generalization error induced by initialization in deep neural networks Y Zhang, ZQJ Xu, T Luo, Z Ma Mathematical and Scientific Machine Learning, 144-164, 2020 | 63 | 2020 |
| A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kinetics T Zhang, Y Yi, Y Xu, ZX Chen, Y Zhang, E Weinan, ZQJ Xu Combustion and Flame 245, 112319, 2022 | 60 | 2022 |
| Causal and structural connectivity of pulse-coupled nonlinear networks D Zhou, Y Xiao, Y Zhang, Z Xu, D Cai Physical review letters 111 (5), 054102, 2013 | 51 | 2013 |
| Granger causality network reconstruction of conductance-based integrate-and-fire neuronal systems D Zhou, Y Xiao, Y Zhang, Z Xu, D Cai PloS one 9 (2), e87636, 2014 | 44 | 2014 |
| MOD-Net: A machine learning approach via model-operator-data network for solving PDEs L Zhang, T Luo, Y Zhang, ZQJ Xu, Z Ma arXiv preprint arXiv:2107.03673, 2021 | 42 | 2021 |
| Embedding principle of loss landscape of deep neural networks Y Zhang, Z Zhang, T Luo, ZJ Xu Advances in Neural Information Processing Systems 34, 14848-14859, 2021 | 39 | 2021 |
| Explicitizing an implicit bias of the frequency principle in two-layer neural networks Y Zhang, ZQJ Xu, T Luo, Z Ma arXiv preprint arXiv:1905.10264, 2019 | 39 | 2019 |
| Towards understanding the condensation of neural networks at initial training H Zhou, Z Qixuan, T Luo, Y Zhang, ZQ Xu Advances in Neural Information Processing Systems 35, 2184-2196, 2022 | 31 | 2022 |
| Embedding Principle: a hierarchical structure of loss landscape of deep neural networks Y Zhang, Y Li, Z Zhang, T Luo, ZQJ Xu Journal of Machine Learning 1 (1), 60-113, 2022 | 31 | 2022 |
| Empirical phase diagram for three-layer neural networks with infinite width H Zhou, Z Qixuan, Z Jin, T Luo, Y Zhang, ZQ Xu Advances in Neural Information Processing Systems 35, 26021-26033, 2022 | 26 | 2022 |
| A linear frequency principle model to understand the absence of overfitting in neural networks Y Zhang, T Luo, Z Ma, ZQJ Xu Chinese Physics Letters 38 (3), 038701, 2021 | 25 | 2021 |
| Analysis of sampling artifacts on the Granger causality analysis for topology extraction of neuronal dynamics D Zhou, Y Zhang, Y Xiao, D Cai Frontiers in computational neuroscience 8, 75, 2014 | 20 | 2014 |
| On the exact computation of linear frequency principle dynamics and its generalization T Luo, Z Ma, ZQJ Xu, Y Zhang SIAM Journal on Mathematics of Data Science 4 (4), 1272-1292, 2022 | 19 | 2022 |
| DLODE: a deep learning-based ODE solver for chemistry kinetics T Zhang, Y Zhang, W E, Y Ju AIAA Scitech 2021 Forum, 1139, 2021 | 11 | 2021 |
| Reliability of the Granger Causality Inference D Zhou, Y Zhang, Y Xiao, D Cai New Journal of Physics 16 (4), 2014 | 11 | 2014 |
Zhi-Qin John XuShanghai Jiao Tong UniversityPotvrđena adresa e-pošte na sjtu.edu.cn