跨层GAT: 分为五个子类,即多级注意力、多通道注意力、多视角注意力、时空注意力和时间序列注意力。 Graph Transformer:分为标准Transformer和GNNTransformer两类。 表1总结了每一个子类的代表性工作。 4.图递归注意网络(GRAN) 4.1 GRU-attention 基于门控循环单元(GRU)的GRAN模型包括: GGNN:在图上引入GRU和软注意...
论文题目:Direct multi-hop Attention based Graph neural Network (现在已被IJCAI-2021接受) 个人注释:该文章的作者单位包括京东AI Research和斯坦福大学,通讯作者是图学习巨佬 Jure Leskovec,于2020年10月2号放在arXiv上,原文链接如下。在本文中,图(graph)和网络(network)的意思是等价的。 https://arxiv.org/ab...
论文：Attention-based Graph Neural Network for semi-supervised learning 代码：dawnranger/pytorch-AGNN...
Here we develop a new self-attention based graph neural network called Hyper-SAGNN applicable to homogeneous and heterogeneous hypergraphs with variable hyperedge sizes. We perform extensive evaluations on multiple datasets, including four benchmark network datasets and two single-cell Hi-C datasets in...
GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloud Hengshuang Zhao Li Jiang Chi-Wing Fu Jiaya Jia The Chinese University of Hong Kong Tencent Youtu Lab 论文地址:https://arxiv.org/abs/1905.08705
Graph Neural Networks LabML. https://nn.labml.ai/graphs/index.html (2023).7.LaBonne, M. Graph Attention Networks: Theoretical and Practical Insights https : / / mlabonne . github.io/blog/posts/2022-03-09-graph_attention_net...
Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement 本文的任务是STS(semantic textual similarity)指给定一个检索句子和比较的句子,计算他们的相似度得分。 过去的模型,把输入的句子独立对待,忽略了句子的上下文交互。attention也就是因此而引入的。
ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs 本文的任务是(sentence pair)句子配对.这包括了Answer Selection(AS),Paraphrase identification(PI),Textual entailment(TE) 基础模型:BCNN(Basic Bi-CNN) BCNN有四个部分:1. 输入层,2. 卷积层,3. 池化层,4. 输出层 ...
[19] Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015) [20] Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation....
To fill the gap, an attention-based graph residual network, a novel structure of Graph Convolutional Neural Network (GCN), was presented to detect human motor intents from raw EEG signals, where the topological structure of EEG electrodes was built as a graph. Meanwhile, deep residual learning...