11 citations · 13 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 2 cited
Reweighting Augmented Samples by Minimizing the Maximal Expected Loss
Mingyang Yi, Lu Hou, Lifeng Shang +3
Data augmentation is an effective technique to improve the generalization of deep neural networks. However, previous data augmentation methods usually treat the augmented samples e…
cs.LG2021
BN-invariant sharpness regularizes the training model to better generalization
Mingyang Yi, Huishuai Zhang, Wei Chen +2
It is arguably believed that flatter minima can generalize better. However, it has been pointed out that the usual definitions of sharpness, which consider either the maxima or the…
cs.LG2019★ 11 cited
Positively Scale-Invariant Flatness of ReLU Neural Networks
Mingyang Yi, Qi Meng, Wei Chen +2
It was empirically confirmed by Keskar et al.\cite{SharpMinima} that flatter minima generalize better. However, for the popular ReLU network, sharp minimum can also generalize well…