212 citations · 457 across the 29 of their papers we have counts for
5 papers · 1 filter
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
Difan Zou, Ziniu Hu, Yewen Wang +3
Graph convolutional networks (GCNs) have recently received wide attentions, due to their successful applications in different graph tasks and different domains. Training GCNs for a…
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo
Bao Wang, Difan Zou, Quanquan Gu +1
As an important Markov Chain Monte Carlo (MCMC) method, stochastic gradient Langevin dynamics (SGLD) algorithm has achieved great success in Bayesian learning and posterior samplin…
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks?
Zixiang Chen, Yuan Cao, Difan Zou +1
A recent line of research on deep learning focuses on the extremely over-parameterized setting, and shows that when the network width is larger than a high degree polynomial of the…
An Improved Analysis of Training Over-parameterized Deep Neural Networks
Difan Zou, Quanquan Gu
A recent line of research has shown that gradient-based algorithms with random initialization can converge to the global minima of the training loss for over-parameterized (i.e., s…
Two Dimension Intensity Distribution of Ultraviolet Scattering Communication
Difan Zou, Zhengyuan Xu, Chen Gong
Consider a ultraviolet (UV) scattering communication system where the position of the transmitter is fixed and the receiver can move around on the ground. To obtain the link gain e…