GPU Occupancy Prediction of Deep Learning Models Using Graph Neural Network Hengquan Mei, Huaizhi Qu, Jingwei Sun, Yanjie Gao, Haoxiang Lin, Guangzhong Sun CLUSTER 2023|October 2023 Published by IEEE The 25th IEEE International Conference on Cluster Computing ...
在考虑上述这些问题的情况下,Liu 等人[10] 提出了一种名为 GraphConsis 的模型,在聚合节点特征时使用了一种采样方法有效的避免潜在的异常邻居。该方法还采用注意机制来聚合不同链路下的邻居信息。因此,学习的节点表示对异常更为鲁棒,GraphConsis 将其作为输入来训练分类器以预测标签。 PS:作者文中没有对这个方法进...
[ICLR 2021] On the Bottleneck of Graph Neural Networks and its Practical Implications openreview.net/forum? github.com/tech-srl/bot) [ICLR 2021] Adaptive Universal Generalized PageRank Graph Neural Network openreview.net/forum? github.com/jianhao2016/ [ICLR 2021] Simple Spectral Graph Convolution ...
The data-science revolution is poised to transform the way photonic systems are simulated and designed. Photonic systems are, in many ways, an ideal substrate for machine learning: the objective of much of computational electromagnetics is the capture of
Very recently, a method embedding protein-protein interaction feature graph directly into the deep neural network structure has also been proposed14. The authors of these methods have demonstrated that incorporating feature relation structures results in better classification performance. However, considering...
A-Lamp:Adaptive layout-aware multi-patch deep convolutionalneural network for photo aesthetic assessment. InCVPR, 2017. [102] Babak Mahdian and Stanislav Saic. Using noise incon-sistencies for blind image forensics. Image and VisionComputing, 27(10):1497–1503, 2009. [103] Owen Mayer and ...
network and enhance its performance (Tang et al., 2018). Another fault diagnosis problem considering bearing of traction motor in high-speed trains is investigated using DBN in (Zou et al., 2021). The learning rate in this method was adaptive. DBNs may be used as the feature extractor ...
6.2 完全自适应特征共享(Fully-Adaptive Feature Sharing) 从另一个极端说起,文献[35]提出了一个自底向上的方法。从瘦网络(thin network)开始,使用对相似任务自动分组的指标,贪心的动态加宽网络。这个加宽的过程动态创建分支,如图4所示。然而这种贪心的做法并不能得到全局的最优。为每个分支分配精确的一个任务,并不...
2021. Graph contrastive learning with adaptive augmentation. In Proceedings of the Web Conference 2021. 2069–2080. 3 PROBLEM DEFINITION 属性图可以被表示为 \mathcal{G}=(\mathcal{V},\mathcal{E},\mathbf{X})=(\mathbf{A},\mathbf{X}) ,这里 \mathcal{V} 是节点集合,节点数 n=\left| \...
关键词:deep learning for graphs, graph neural networks, learning for structured data 1. Introduction 图深度学习上,有关的挑战有: 首先,模型应该能够自适应样本容量和图的拓扑结构变化。 其次,很难获取关于节点 ID 和多个样本之间顺序的信息。 另外,图是离散的对象,这对可微性造成了限制,也限制了穷举搜索方法...