受强制性开放获取政策约束的文章 - Mo Zhou了解详情
可在其他位置公开访问的文章：6 篇
Toward Understanding the Importance of Noise in Training Neural Networks
M Zhou, T Liu, Y Li, D Lin, E Zhou, T Zhao
International Conference on Machine Learning, 7594-7602, 2019
强制性开放获取政策： US National Science Foundation
A local convergence theory for mildly over-parameterized two-layer neural network
M Zhou, R Ge, C Jin
Conference on Learning Theory, 4577-4632, 2021
强制性开放获取政策： US National Science Foundation
Understanding Deflation Process in Over-parametrized Tensor Decomposition
R Ge, Y Ren, X Wang, M Zhou
Advances in Neural Information Processing Systems 34, 1299-1311, 2021
强制性开放获取政策： US National Science Foundation
Plateau in Monotonic Linear Interpolation--A" Biased" View of Loss Landscape for Deep Networks
X Wang, AN Wang, M Zhou, R Ge
International Conference on Learning Representations, 2023
强制性开放获取政策： US National Science Foundation
Understanding The Robustness of Self-supervised Learning Through Topic Modeling
Z Luo, S Wu, C Weng, M Zhou, R Ge
International Conference on Learning Representations, 2023
强制性开放获取政策： US National Science Foundation
Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression
M Zhou, R Ge
International Conference on Machine Learning, 2023
强制性开放获取政策： US National Science Foundation
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