activity
20182020
most citedTrained Rank Pruning for Efficient Deep Neural Networks

13 citations · 24 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CV20203 cited

Automated Radiological Report Generation For Chest X-Rays With Weakly-Supervised End-to-End Deep Learning

Shuai Zhang, Xiaoyan Xin, Yang Wang +7

The chest X-Ray (CXR) is the one of the most common clinical exam used to diagnose thoracic diseases and abnormalities. The volume of CXR scans generated daily in hospitals is huge…

cs.CV2019

Weakly-Supervised Degree of Eye-Closeness Estimation

Eyasu Mequanint, Shuai Zhang, Bijan Forutanpour +2

Following recent technological advances there is a growing interest in building non-intrusive methods that help us communicate with computing devices. In this regard, accurate info…

cs.CV201913 cited

Trained Rank Pruning for Efficient Deep Neural Networks

Yuhui Xu, Yuxi Li, Shuai Zhang +7

To accelerate DNNs inference, low-rank approximation has been widely adopted because of its solid theoretical rationale and efficient implementations. Several previous works attemp…

cs.LG20198 cited

AutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz Continuous Penalty in Deep Convolutional Neural Networks

Jiancheng Lyu, Shuai Zhang, Yingyong Qi +1

ShuffleNet is a state-of-the-art light weight convolutional neural network architecture. Its basic operations include group, channel-wise convolution and channel shuffling. However…

cs.CV2018

DAC: Data-free Automatic Acceleration of Convolutional Networks

Xin Li, Shuai Zhang, Bolan Jiang +3

Deploying a deep learning model on mobile/IoT devices is a challenging task. The difficulty lies in the trade-off between computation speed and accuracy. A complex deep learning mo…

cs.LG2018

DNQ: Dynamic Network Quantization

Yuhui Xu, Shuai Zhang, Yingyong Qi +3

Network quantization is an effective method for the deployment of neural networks on memory and energy constrained mobile devices. In this paper, we propose a Dynamic Network Quant…