13 citations · 22 across the 7 of their papers we have counts for
7 papers
Towards Robust Knowledge Graph Embedding via Multi-task Reinforcement Learning
Zhao Zhang, Fuzhen Zhuang, Hengshu Zhu +4
Nowadays, Knowledge graphs (KGs) have been playing a pivotal role in AI-related applications. Despite the large sizes, existing KGs are far from complete and comprehensive. In orde…
A Channel-Aware Routing Protocol With Nearest Neighbor Regression For Underwater Sensor Networks
Boyu Diao, Chao Li, Qi Wang +2
The underwater acoustic channel is one of the most challenging communication channels. Due to periodical tidal and daily climatic variation, underwater noise is periodically fluctu…
A Distributed SGD Algorithm with Global Sketching for Deep Learning Training Acceleration
LingFei Dai, Boyu Diao, Chao Li +1
Distributed training is an effective way to accelerate the training process of large-scale deep learning models. However, the parameter exchange and synchronization of distributed…
Channel Pruning via Multi-Criteria based on Weight Dependency
Yangchun Yan, Rongzuo Guo, Chao Li +2
Channel pruning has demonstrated its effectiveness in compressing ConvNets. In many related arts, the importance of an output feature map is only determined by its associated filte…
PFGDF: Pruning Filter via Gaussian Distribution Feature for Deep Neural Networks Acceleration
Jianrong Xu, Boyu Diao, Bifeng Cui +3
Deep learning has achieved impressive results in many areas, but the deployment of edge intelligent devices is still very slow. To solve this problem, we propose a novel compressio…
Gated Convolutional Networks with Hybrid Connectivity for Image Classification
Chuanguang Yang, Zhulin An, Hui Zhu +5
We propose a simple yet effective method to reduce the redundancy of DenseNet by substantially decreasing the number of stacked modules by replacing the original bottleneck by our…
Multi-Objective Pruning for CNNs Using Genetic Algorithm
Chuanguang Yang, Zhulin An, Chao Li +2
In this work, we propose a heuristic genetic algorithm (GA) for pruning convolutional neural networks (CNNs) according to the multi-objective trade-off among error, computation and…