665 citations · 2.1k across the 57 of their papers we have counts for
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DRGCN: Dynamic Evolving Initial Residual for Deep Graph Convolutional Networks
Lei Zhang, Xiaodong Yan, Jianshan He +2
Graph convolutional networks (GCNs) have been proved to be very practical to handle various graph-related tasks. It has attracted considerable research interest to study deep GCNs,…
Winograd Convolution: A Perspective from Fault Tolerance
Xinghua Xue, Haitong Huang, Cheng Liu +3
Winograd convolution is originally proposed to reduce the computing overhead by converting multiplication in neural network (NN) with addition via linear transformation. Other than…
LocalDrop: A Hybrid Regularization for Deep Neural Networks
Ziqing Lu, Chang Xu, Bo Du +3
In neural networks, developing regularization algorithms to settle overfitting is one of the major study areas. We propose a new approach for the regularization of neural networks…
Attention-based Convolutional Neural Network for Weakly Labeled Human Activities Recognition with Wearable Sensors
Kun Wang, Jun He, Lei Zhang
Unlike images or videos data which can be easily labeled by human being, sensor data annotation is a time-consuming process. However, traditional methods of human activity recognit…