2 citations · 2 across the 1 of their papers we have counts for
5 papers
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…
TernaryBERT: Distillation-aware Ultra-low Bit BERT
Wei Zhang, Lu Hou, Yichun Yin +4
Transformer-based pre-training models like BERT have achieved remarkable performance in many natural language processing tasks.However, these models are both computation and memory…
DynaBERT: Dynamic BERT with Adaptive Width and Depth
Lu Hou, Zhiqi Huang, Lifeng Shang +3
The pre-trained language models like BERT, though powerful in many natural language processing tasks, are both computation and memory expensive. To alleviate this problem, one appr…
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…