4.4k citations · 7.7k across the 83 of their papers we have counts for
12 papers · 1 filter
DropNAS: Grouped Operation Dropout for Differentiable Architecture Search
Weijun Hong, Guilin Li, Weinan Zhang +4
Neural architecture search (NAS) has shown encouraging results in automating the architecture design. Recently, DARTS relaxes the search process with a differentiable formulation t…
Federated Learning with Positive and Unlabeled Data
Xinyang Lin, Hanting Chen, Yixing Xu +4
We study the problem of learning from positive and unlabeled (PU) data in the federated setting, where each client only labels a little part of their dataset due to the limitation…
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…
DC-NAS: Divide-and-Conquer Neural Architecture Search
Yunhe Wang, Yixing Xu, Dacheng Tao
Most applications demand high-performance deep neural architectures costing limited resources. Neural architecture searching is a way of automatically exploring optimal deep neural…
Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks
Yehui Tang, Yunhe Wang, Yixing Xu +4
Deep neural networks often consist of a great number of trainable parameters for extracting powerful features from given datasets. On one hand, massive trainable parameters signifi…
On Positive-Unlabeled Classification in GAN
Tianyu Guo, Chang Xu, Jiajun Huang +4
This paper defines a positive and unlabeled classification problem for standard GANs, which then leads to a novel technique to stabilize the training of the discriminator in GANs.…
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…
ReNAS:Relativistic Evaluation of Neural Architecture Search
Yixing Xu, Yunhe Wang, Kai Han +4
An effective and efficient architecture performance evaluation scheme is essential for the success of Neural Architecture Search (NAS). To save computational cost, most of existing…
Searching for Accurate Binary Neural Architectures
Mingzhu Shen, Kai Han, Chunjing Xu +1
Binary neural networks have attracted tremendous attention due to the efficiency for deploying them on mobile devices. Since the weak expression ability of binary weights and featu…
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…