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Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate

Nov 04, 2020
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu

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Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling

Oct 19, 2020
Difan Zou, Pan Xu, Quanquan Gu

* 42 pages, 1 figure 

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On the Global Convergence of Training Deep Linear ResNets

Mar 02, 2020
Difan Zou, Philip M. Long, Quanquan Gu

* 26 pages, 1 figure. In ICLR 2020 

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How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks?

Nov 27, 2019
Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu

* 27 pages 

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Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks

Nov 17, 2019
Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu

* Published in NeurIPS 2019 

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Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo

Nov 02, 2019
Bao Wang, Difan Zou, Quanquan Gu, Stanley Osher

* 27 pages, 5 figures 

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An Improved Analysis of Training Over-parameterized Deep Neural Networks

Jun 11, 2019
Difan Zou, Quanquan Gu

* 30 pages, 1 figure, 1 table 

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Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks

Nov 21, 2018
Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu

* 47 pages 

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Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization

Feb 19, 2018
Pan Xu, Jinghui Chen, Difan Zou, Quanquan Gu

* 36 pages, 1 figure, 1 table 

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Stochastic Variance-Reduced Hamilton Monte Carlo Methods

Feb 13, 2018
Difan Zou, Pan Xu, Quanquan Gu

* 16 pages, 3 figures, 4 tables 

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Saving Gradient and Negative Curvature Computations: Finding Local Minima More Efficiently

Dec 11, 2017
Yaodong Yu, Difan Zou, Quanquan Gu

* 31 pages, 1 table 

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