A linear frequency principle model to understand the absence of overfitting in neural networks
Why heavily parameterized neural networks (NNs) do not overfit the data is an important
long standing open question. We propose a phenomenological model of the NN training to …
long standing open question. We propose a phenomenological model of the NN training to …
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, 2021 - inis.iaea.org
[en] Why heavily parameterized neural networks (NNs) do not overfit the data is an important
long standing open question. We propose a phenomenological model of the NN training to …
long standing open question. We propose a phenomenological model of the NN training to …
[PDF][PDF] Linear Frequency Principle Model to Understand the Absence of Overfitting in Neural Networks
Y Zhang, T Luo, Z Ma, ZQJ Xu - qiniu.pattern.swarma.org
Why heavily parameterized neural networks (NNs) do not overfit the data is an important
long standing open question. We propose a phenomenological model of the NN training to …
long standing open question. We propose a phenomenological model of the NN training to …
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, 2021 - ui.adsabs.harvard.edu
Why heavily parameterized neural networks (NNs) do not overfit the data is an important
long standing open question. We propose a phenomenological model of the NN training to …
long standing open question. We propose a phenomenological model of the NN training to …
Linear Frequency Principle Model to Understand the Absence of Overfitting in Neural Networks
Y Zhang, T Luo, Z Ma, ZQJ Xu - arXiv preprint arXiv:2102.00200, 2021 - arxiv.org
Why heavily parameterized neural networks (NNs) do not overfit the data is an important
long standing open question. We propose a phenomenological model of the NN training to …
long standing open question. We propose a phenomenological model of the NN training to …
[PDF][PDF] Linear Frequency Principle Model to Understand the Absence of Overfitting in Neural Networks
Y Zhang, T Luo, Z Ma, ZQJ Xu - researchgate.net
Why heavily parameterized neural networks (NNs) do not overfit the data is an important
long standing open question. We propose a phenomenological model of the NN training to …
long standing open question. We propose a phenomenological model of the NN training to …
[HTML][HTML] A Linear Frequency Principle Model to Understand the Absence of Overfitting in Neural Networks
Z Yaoyu - Chinese Physics Letters, 2021 - cpl.iphy.ac.cn
* Corresponding author. Email: xuzhiqin@ sjtu. edu. cn Citation Text: Zhang YY, Luo T, Ma
Z, and Xu ZQ 2021 Chin. Phys. Lett. 38 038701 PDF PDF (Mobile) Abstract Why heavily …
Z, and Xu ZQ 2021 Chin. Phys. Lett. 38 038701 PDF PDF (Mobile) Abstract Why heavily …
A Linear Frequency Principle Model to Understand the Absence of Overfitting in Neural Networks
Z Yaoyu, L Tao, M Zheng, JX Zhi-Qin - Chinese Physics Letters, 2021 - cpl.iphy.ac.cn
Why heavily parameterized neural networks (NNs) do not overfit the data is an important
long standing open question. We propose a phenomenological model of the NN training to …
long standing open question. We propose a phenomenological model of the NN training to …