4 citations · 4 across the 2 of their papers we have counts for
3 papers
cs.LG2020
Weighted Aggregating Stochastic Gradient Descent for Parallel Deep Learning
Pengzhan Guo, Zeyang Ye, Keli Xiao +1
This paper investigates the stochastic optimization problem with a focus on developing scalable parallel algorithms for deep learning tasks. Our solution involves a reformation of…
cs.CV2018
Stop memorizing: A data-dependent regularization framework for intrinsic pattern learning
Wei Zhu, Qiang Qiu, Bao Wang +3
Deep neural networks (DNNs) typically have enough capacity to fit random data by brute force even when conventional data-dependent regularizations focusing on the geometry of the f…
cs.CV2017★ 4 cited
LDMNet: Low Dimensional Manifold Regularized Neural Networks
Wei Zhu, Qiang Qiu, Jiaji Huang +3
Deep neural networks have proved very successful on archetypal tasks for which large training sets are available, but when the training data are scarce, their performance suffers f…