13 citations · 19 across the 5 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2021★ 3 cited
CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks
Haotian Xue, Kaixiong Zhou, Tianlong Chen +4
Despite the recent advances of graph neural networks (GNNs) in modeling graph data, the training of GNNs on large datasets is notoriously hard due to the overfitting. Adversarial t…
cs.LG2018
Gradient-Coherent Strong Regularization for Deep Neural Networks
Dae Hoon Park, Chiu Man Ho, Yi Chang +1
Regularization plays an important role in generalization of deep neural networks, which are often prone to overfitting with their numerous parameters. L1 and L2 regularizers are co…
cs.LG2018
Sequenced-Replacement Sampling for Deep Learning
Chiu Man Ho, Dae Hoon Park, Wei Yang +1
We propose sequenced-replacement sampling (SRS) for training deep neural networks. The basic idea is to assign a fixed sequence index to each sample in the dataset. Once a mini-bat…