Cong Fang
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Spider: Near-optimal non-convex optimization via stochastic path-integrated differential estimator
C Fang, CJ Li, Z Lin, T Zhang
Advances in Neural Information Processing Systems 31, 2018
Sharp analysis for nonconvex sgd escaping from saddle points
C Fang, Z Lin, T Zhang
Conference on Learning Theory, 1192-1234, 2019
A sharp convergence rate analysis for distributed accelerated gradient methods
H Li, C Fang, W Yin, Z Lin
arXiv preprint arXiv:1810.01053, 2018
A robust hybrid method for text detection in natural scenes by learning-based partial differential equations
Z Zhao, C Fang, Z Lin, Y Wu
Neurocomputing 168, 23-34, 2015
Modeling from features: a mean-field framework for over-parameterized deep neural networks
C Fang, J Lee, P Yang, T Zhang
Conference on learning theory, 1887-1936, 2021
Accelerated optimization for machine learning
Z Lin, H Li, C Fang
Nature Singapore: Springer, 2020
Complexities in projection-free stochastic non-convex minimization
Z Shen, C Fang, P Zhao, J Huang, H Qian
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
Dictionary learning with structured noise
P Zhou, C Fang, Z Lin, C Zhang, EY Chang
Neurocomputing 273, 414-423, 2018
Decentralized accelerated gradient methods with increasing penalty parameters
H Li, C Fang, W Yin, Z Lin
IEEE transactions on Signal Processing 68, 4855-4870, 2020
Feature learning via partial differential equation with applications to face recognition
C Fang, Z Zhao, P Zhou, Z Lin
Pattern Recognition 69, 14-25, 2017
Lifted proximal operator machines
J Li, C Fang, Z Lin
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 4181-4188, 2019
Hessian-aware zeroth-order optimization for black-box adversarial attack
H Ye, Z Huang, C Fang, CJ Li, T Zhang
arXiv preprint arXiv:1812.11377, 2018
Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training
C Fang, H He, Q Long, WJ Su
Proceedings of the National Academy of Sciences 118 (43), e2103091118, 2021
Accelerated first-order optimization algorithms for machine learning
H Li, C Fang, Z Lin
Proceedings of the IEEE 108 (11), 2067-2082, 2020
Convex formulation of overparameterized deep neural networks
C Fang, Y Gu, W Zhang, T Zhang
arXiv preprint arXiv:1911.07626, 2019
Over parameterized two-level neural networks can learn near optimal feature representations
C Fang, H Dong, T Zhang
arXiv preprint arXiv:1910.11508, 2019
Training neural networks by lifted proximal operator machines
J Li, M Xiao, C Fang, Y Dai, C Xu, Z Lin
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
Faster and non-ergodic O (1/k) stochastic alternating direction method of multipliers
C Fang, F Cheng, Z Lin
Advances in Neural Information Processing Systems 30, 2017
Layer-peeled model: Toward understanding well-trained deep neural networks
C Fang, H He, Q Long, WJ Su
Mathematical models of overparameterized neural networks
C Fang, H Dong, T Zhang
Proceedings of the IEEE 109 (5), 683-703, 2021
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