Zobrazeno 1 - 10
of 188
pro vyhledávání: '"Tian Zhaoshuo"'
Graph Neural Networks (GNNs) have emerged as the most powerful weapon for various graph tasks due to the message-passing mechanism's great local information aggregation ability. However, over-smoothing has always hindered GNNs from going deeper and c
Externí odkaz:
http://arxiv.org/abs/2403.15520
Recently, Graph Transformers have emerged as a promising solution to alleviate the inherent limitations of Graph Neural Networks (GNNs) and enhance graph representation performance. Unfortunately, Graph Transformers are computationally expensive due
Externí odkaz:
http://arxiv.org/abs/2403.15480
Autor:
Du, Haiwen, Ju, Zheng, An, Yu, Du, Honghui, Zhu, Dongjie, Tian, Zhaoshuo, Lawlor, Aonghus, Dong, Ruihai
Spectrum analysis systems in online water quality testing are designed to detect types and concentrations of pollutants and enable regulatory agencies to respond promptly to pollution incidents. However, spectral data-based testing devices suffer fro
Externí odkaz:
http://arxiv.org/abs/2308.01138
Spectral detection technology, as a non-invasive method for rapid detection of substances, combined with deep learning algorithms, has been widely used in food detection. However, in real scenarios, acquiring and labeling spectral data is an extremel
Externí odkaz:
http://arxiv.org/abs/2210.12440
Currently, it is a hot research topic to realize accurate, efficient, and real-time identification of massive spectral data with the help of deep learning and IoT technology. Deep neural networks played a key role in spectral analysis. However, the i
Externí odkaz:
http://arxiv.org/abs/2206.12420
Publikováno v:
In Neural Networks January 2025 181
Publikováno v:
In Environmental Technology & Innovation August 2024 35
Autor:
Geng, Chenchen, Zhang, Min, Wei, Hang, Gu, Jinxin, Zhao, Tao, Guan, Huan, Liang, Shuhui, Boytsova, Olga, Dou, Shuliang, Chen, Yanyu, Li, Yao, Tian, Zhaoshuo
Publikováno v:
In Solar Energy Materials and Solar Cells 1 August 2024 272
Publikováno v:
Neurocomputing,2022
Recently, leveraging different channels to model social semantic information and using self-supervised learning tasks to boost recommendation performance has been proven to be a very promising work. However, how to deeply dig out the relationship bet
Externí odkaz:
http://arxiv.org/abs/2109.00676
The attention mechanism enables graph neural networks (GNNs) to learn the attention weights between the target node and its one-hop neighbors, thereby improving the performance further. However, most existing GNNs are oriented toward homogeneous grap
Externí odkaz:
http://arxiv.org/abs/2106.09289