Ma Chao
Ma Chao
Department of Mathematics, Stanford University
Verified email at - Homepage
Cited by
Cited by
Towards theoretically understanding why sgd generalizes better than adam in deep learning
P Zhou, J Feng, C Ma, C Xiong, S Hoi
Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020
How sgd selects the global minima in over-parameterized learning: A dynamical stability perspective
L Wu, C Ma
Advances in Neural Information Processing Systems 31, 2018
The Barron Space and the Flow-Induced Function Spaces for Neural Network Models
E Weinan, C Ma, L Wu
Constructive Approximation,, 2021
A priori estimates of the population risk for two-layer neural networks
C Ma, L Wu
Communications in Mathematical Sciences, 2019 17 (5), 1407-1425, 2018
Towards a Mathematical Understanding of Neural Network-Based Machine Learning: what we know and what we don't
C Ma, S Wojtowytsch, L Wu
CSIAM Trans. Appl. Math. 1 (4), 561-615, 2020
Bispectrum inversion with application to multireference alignment
T Bendory, N Boumal, C Ma, Z Zhao, A Singer
IEEE Transactions on signal processing 66 (4), 1037-1050, 2017
A comparative analysis of the optimization and generalization property of two-layer neural network and random feature models under gradient descent dynamics
C Ma, L Wu
Science China Mathematics 63 (No. 7), 1235–1258, 2019
A mean field analysis of deep resnet and beyond: Towards provably optimization via overparameterization from depth
Y Lu, C Ma, Y Lu, J Lu, L Ying
International Conference on Machine Learning, 6426-6436, 2020
Uniformly Accurate Machine Learning Based Hydrodynamic Models for Kinetic Equations
J Han, C Ma, Z Ma, W E
Proceedings of the National Academy of Sciences, 2019, 2019
Model reduction with memory and the machine learning of dynamical systems
C Ma, J Wang
Commun. Comput. Phys., 25 (2019), pp. 947-962., 2018
Machine learning from a continuous viewpoint, I
C Ma, L Wu
Science China Mathematics 63 (11), 2233-2266, 2020
Modeling subgrid-scale force and divergence of heat flux of compressible isotropic turbulence by artificial neural network
C Xie, K Li, C Ma, J Wang
Physical Review Fluids 4 (10), 104605, 2019
Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence
C Xie, J Wang, K Li, C Ma
Rademacher complexity and the generalization error of residual networks
C Ma, Q Wang
Communications in Mathematical Sciences 18 (6), 1755-1774, 2020
On linear stability of sgd and input-smoothness of neural networks
C Ma, L Ying
Advances in Neural Information Processing Systems 34, 16805-16817, 2021
Global convergence of gradient descent for deep linear residual networks
L Wu, Q Wang, C Ma
Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019
The Multiscale Structure of Neural Network Loss Functions: The Effect on Optimization and Origin
C Ma, D Kunin, L Wu, L Ying
arXiv preprint arXiv:2204.11326, 2022
Globally convergent Levenberg-Marquardt method for phase retrieval
C Ma, X Liu, Z Wen
IEEE Transactions on Information Theory 65 (4), 2343-2359, 2018
Beyond the quadratic approximation: the multiscale structure of neural network loss landscapes
C Ma, D Kunin, L Wu, L Ying
arXiv preprint arXiv:2204.11326, 2022
Heterogeneous multireference alignment for images with application to 2D classification in single particle reconstruction
C Ma, T Bendory, N Boumal, F Sigworth, A Singer
IEEE Transactions on Image Processing 29, 1699-1710, 2019
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