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Lei Wu (吴磊)
Lei Wu (吴磊)
Assistant Professor, Peking University
Verified email at math.pku.edu.cn - Homepage
Title
Cited by
Cited by
Year
The anisotropic noise in stochastic gradient descent: Its behavior of escaping from sharp minima and regularization effects
Z Zhu, J Wu, B Yu, L Wu, J Ma
International Conference on Machine Learning (ICML 2019), 2018
2102018
How SGD selects the global minima in over-parameterized learning: A dynamical stability perspective
L Wu, C Ma, E Weinan
Advances in Neural Information Processing Systems (NeurIPS 2018), 2018
2042018
Towards understanding generalization of deep learning: perspective of loss landscapes
L Wu, Z Zhu, W E
ICML 2017 Workshop on Principled Approaches to Deep Learning, 2017, 2017
1982017
The Barron space and the flow-induced function spaces for neural network models
W E, C Ma, L Wu
Constructive Approximation 55 (1), 369-406, 2022
175*2022
Towards understanding and improving the transferability of adversarial examples in deep neural networks
L Wu, Z Zhu
Asian Conference on Machine Learning, 837-850, 2020
150*2020
A priori estimates of the population risk for two-layer neural networks
W E, C Ma, L Wu
Communications in Mathematical Sciences 17 (5), 1407-1425, 2019
112*2019
Towards a mathematical understanding of neural network-based machine learning: what we know and what we don't
W E, C Ma, S Wojtowytsch, L Wu
CSIAM Transactions on Applied Mathematics 1 (4), 561--615, 2020
110*2020
A comparative analysis of optimization and generalization properties of two-layer neural network and random feature models under gradient descent dynamics
E Weinan, C Ma, L Wu
Sci. China Math, 2019
942019
Machine learning from a continuous viewpoint, I
C Ma, L Wu
Science China Mathematics 63 (11), 2233-2266, 2020
662020
Beyond the quadratic approximation: the multiscale structure of neural network loss landscapes
C Ma, D Kunin, L Wu, L Ying
Journal of Machine Learning, 2022, 2022
45*2022
The anisotropic noise in stochastic gradient descent: Its behavior of escaping from minima and regularization effects
Z Zhu, J Wu, B Yu, L Wu, J Ma
442018
Irreversible samplers from jump and continuous Markov processes
YA Ma, EB Fox, T Chen, L Wu
Statistics and Computing, 1-26, 2018
40*2018
The alignment property of SGD noise and how it helps select flat minima: A stability analysis
L Wu, M Wang, WJ Su
NeurIPS 2022, 2022
30*2022
Global Convergence of Gradient Descent for Deep Linear Residual Networks
L Wu, Q Wang, C Ma
Advances in Neural Information Processing Systems (NeurIPS 2019), 2019
272019
Machine learning based non-Newtonian fluid model with molecular fidelity
H Lei, L Wu, W E
Physical Review E 102 (4), 043309, 2020
202020
Complexity measures for neural networks with general activation functions using path-based norms
Z Li, C Ma, L Wu
arXiv preprint arXiv:2009.06132, 2020
172020
Approximation analysis of convolutional neural networks
C Bao, Q Li, Z Shen, C Tai, L Wu, X Xiang
work 65, 871, 2014
152014
The Slow Deterioration of the Generalization Error of the Random Feature Model
C Ma, L Wu, W E
Mathematical and Scientific Machine Learning (MSML) 2020, 2020
142020
Learning a single neuron for non-monotonic activation functions
L Wu
AISTATS 2022, 2022
132022
The quenching-activation behavior of the gradient descent dynamics for two-layer neural network models
C Ma, L Wu, W E
arXiv preprint arXiv:2006.14450, 2020
132020
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