23 citations · 57 across the 6 of their papers we have counts for
5 papers · 1 filter
Winograd Algorithm for AdderNet
Wenshuo Li, Hanting Chen, Mingqiang Huang +3
Adder neural network (AdderNet) is a new kind of deep model that replaces the original massive multiplications in convolutions by additions while preserving the high performance. S…
AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence
Yunhe Wang, Mingqiang Huang, Kai Han +4
Convolutional neural networks (CNN) have been widely used for boosting the performance of many machine intelligence tasks. However, the CNN models are usually computationally inten…
Positive-Unlabeled Compression on the Cloud
Yixing Xu, Yunhe Wang, Hanting Chen +4
Many attempts have been done to extend the great success of convolutional neural networks (CNNs) achieved on high-end GPU servers to portable devices such as smart phones. Providin…
Data-Free Learning of Student Networks
Hanting Chen, Yunhe Wang, Chang Xu +6
Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones…
Learning Student Networks via Feature Embedding
Hanting Chen, Yunhe Wang, Chang Xu +2
Deep convolutional neural networks have been widely used in numerous applications, but their demanding storage and computational resource requirements prevent their applications on…