Ma Chao

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Lei Wu (吴磊)Assistant Professor, Peking UniversityAdresă de e-mail confirmată pe math.pku.edu.cn
Lexing YingProfessor of Mathematics, Stanford UniversityAdresă de e-mail confirmată pe stanford.edu
Weinan EProfessor of Mathematics, Princeton UniversityAdresă de e-mail confirmată pe math.princeton.edu
Jianchun WangSouthern University of Science and Technology, Shenzhen, ChinaAdresă de e-mail confirmată pe sustech.edu.cn
Pan ZhouAssistant Professor at SMUAdresă de e-mail confirmată pe smu.edu.sg
Amit SingerPrinceton UniversityAdresă de e-mail confirmată pe math.princeton.edu
Tamir BendoryAssociate ProfessorAdresă de e-mail confirmată pe tauex.tau.ac.il
Nicolas BoumalEcole Polytechnique Fédérale de Lausanne (EPFL)Adresă de e-mail confirmată pe epfl.ch
Zhizhen (Jane) ZhaoDepartment of Electrical and Computer Engineering, University of Illinois at Urbana ChampaignAdresă de e-mail confirmată pe illinois.edu
Yulong LuAssistant Professor at University of Minnesota Twin CitiesAdresă de e-mail confirmată pe umn.edu
Jianfeng Lu 鲁剑锋Department of Mathematics, Department of Chemistry and Department of Physics, Duke UniversityAdresă de e-mail confirmată pe math.duke.edu
Jiequn HanFlatiron Institute, Simons FoundationAdresă de e-mail confirmată pe flatironinstitute.org
Zaiwen WenPeking UniversityAdresă de e-mail confirmată pe pku.edu.cn
Xin LIUAcademy of Mathematics and Systems Science, Chinese Academy of SciencesAdresă de e-mail confirmată pe lsec.cc.ac.cn

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Ma Chao

Department of Mathematics, Stanford University

Adresă de e-mail confirmată pe stanford.edu - Pagina de pornire

machine learning applied math


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
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	309	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	245	2018
The Barron Space and the Flow-Induced Function Spaces for Neural Network Models E Weinan, C Ma, L Wu Constructive Approximation, https://doi.org/10.1007/s00365-021-09549-y, 2021	223*	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	133	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	132	2020
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	108*	2019
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	103	2017
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	95	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	93	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	83	2018
Machine learning from a continuous viewpoint, I C Ma, L Wu Science China Mathematics 63 (11), 2233-2266, 2020	72	2020
Artificial neural network approach to large-eddy simulation of compressible isotropic turbulence C Xie, J Wang, K Li, C Ma PHYSICAL REVIEW E, 2019	69	2019
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	66	2019
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	61*	2021
Rademacher complexity and the generalization error of residual networks C Ma, Q Wang Communications in Mathematical Sciences 18 (6), 1755-1774, 2020	61*	2020
Globally convergent Levenberg-Marquardt method for phase retrieval C Ma, X Liu, Z Wen IEEE Transactions on Information Theory 65 (4), 2343-2359, 2018	32	2018
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	31	2019
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	30	2022
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	28	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	28	2019

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