论文解读（SUGRL）《Simple Unsupervised Graph Representation Learning》

【论文解读（SUGRL）《Simple Unsupervised Graph Representation Learning》】的更多相关文章

论文解读（SUGRL）《Simple Unsupervised Graph Representation Learning》

Paper Information Title:Simple Unsupervised Graph Representation LearningAuthors: Yujie Mo.Liang Peng.Jie Xu, Xiaoshuang Shi.Xiaofeng ZhuSources:2022 AAAIPaper:downloadCode:download Abstract 作者提出了一种简单的无监督图表示学习方法来进行有效和高效的对比学习.具体而言,通过构造多重损失探索结构信息与邻域信息之…

论文解读GALA《Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning》

论文信息 Title:<Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning> Authors:Jiwoong Park.Minsik Lee.H. Chang.Kyuewang Lee.J. Choi Sources:2019 IEEE/CVF International Conference on Computer Vision (ICCV) Paper:Downlo…

论文解读（JKnet）《Representation Learning on Graphs with Jumping Knowledge Networks》

论文信息论文标题:Representation Learning on Graphs with Jumping Knowledge Networks论文作者:Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka论文来源:2018,ICML论文地址:download论文代码:download 1 Introduction 最近,图表示学习提出了基于 "邻域聚…

论文解读（USIB）《Towards Explanation for Unsupervised Graph-Level Representation Learning》

论文信息论文标题:Towards Explanation for Unsupervised Graph-Level Representation Learning论文作者:Qinghua Zheng, Jihong Wang, Minnan Luo, Yaoliang Yu, Jundong Li, Lina Yao, Xiaojun Chang论文来源:2022, arXiv论文地址:download论文代码:download 1 Introduction 使用信息瓶颈的图级表示可解释性.…

论文解读《Momentum Contrast for Unsupervised Visual Representation Learning》俗称 MoCo

论文题目:<Momentum Contrast for Unsupervised Visual Representation Learning> 论文作者: Kaiming He.Haoqi Fan. Yuxin Wu. Saining Xie. Ross Girshick 论文来源:arXiv 论文来源:https://github.com/facebookresearch/moco 1 主要思想文章核心思想是使用基于 Contrastive learning 的方式自监督的训练一个图片表…

论文解读（GMI）《Graph Representation Learning via Graphical Mutual Information Maximization》2

Paper Information 论文作者:Zhen Peng.Wenbing Huang.Minnan Luo.Q. Zheng.Yu Rong.Tingyang Xu.Junzhou Huang论文来源:WWW 2020论文地址:download代码地址:download 前言 1.自监督学习(Self-supervised):属于无监督学习,其核心是自动为数据打标签(伪标签或其他角度的可信标签,包括图像的旋转.分块等等),通过让网络按照既定的规则,对数据打出正确的标签来更好地进行特征表示…

论文解读（GMI）《Graph Representation Learning via Graphical Mutual Information Maximization》

Paper Information 论文作者:Zhen Peng.Wenbing Huang.Minnan Luo.Q. Zheng.Yu Rong.Tingyang Xu.Junzhou Huang论文来源:WWW 2020论文地址:download代码地址:download 前言 1.自监督学习(Self-supervised):属于无监督学习,其核心是自动为数据打标签(伪标签或其他角度的可信标签,包括图像的旋转.分块等等),通过让网络按照既定的规则,对数据打出正确的标签来更好地进行特征表示…