74 citations · 112 across the 19 of their papers we have counts for
7 papers · 1 filter
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…
Knowledge Distillation Using Hierarchical Self-Supervision Augmented Distribution
Chuanguang Yang, Zhulin An, Linhang Cai +1
Knowledge distillation (KD) is an effective framework that aims to transfer meaningful information from a large teacher to a smaller student. Generally, KD often involves how to de…
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…
Heterogeneous Graph Neural Network with Multi-view Representation Learning
Zezhi Shao, Yongjun Xu, Wei Wei +3
Graph neural networks for heterogeneous graph embedding is to project nodes into a low-dimensional space by exploring the heterogeneity and semantics of the heterogeneous graph. Ho…
Hierarchical Self-supervised Augmented Knowledge Distillation
Chuanguang Yang, Zhulin An, Linhang Cai +1
Knowledge distillation often involves how to define and transfer knowledge from teacher to student effectively. Although recent self-supervised contrastive knowledge achieves the b…