21 citations · 36 across the 5 of their papers we have counts for
4 papers · 1 filter
Improved OOD Generalization via Adversarial Training and Pre-training
Mingyang Yi, Lu Hou, Jiacheng Sun +4
Recently, learning a model that generalizes well on out-of-distribution (OOD) data has attracted great attention in the machine learning community. In this paper, after defining OO…
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…
Power Law in Sparsified Deep Neural Networks
Lu Hou, James T. Kwok
The power law has been observed in the degree distributions of many biological neural networks. Sparse deep neural networks, which learn an economical representation from the data,…
Loss-aware Weight Quantization of Deep Networks
Lu Hou, James T. Kwok
The huge size of deep networks hinders their use in small computing devices. In this paper, we consider compressing the network by weight quantization. We extend a recently propose…