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摘要: 论文信息 论文标题:Self-supervised Learning on Graphs: Contrastive, Generative,or Predictive论文作者:Lirong Wu, Haitao Lin, Cheng Tan,Zhangyang Gao, and Stan.Z.Li论 阅读全文
posted @ 2022-04-06 16:50 Learner- 阅读(38) 评论(0) 推荐(0) 编辑
摘要: 论文信息 论文标题:Contrastive Self-supervised Learning for Graph Classification论文作者:Jiaqi Zeng, Pengtao Xie论文来源:2020, AAAI论文地址:download 论文代码:download Abstract 阅读全文
posted @ 2022-04-06 11:09 Learner- 阅读(44) 评论(0) 推荐(0) 编辑
摘要: 论文信息 论文标题:Supervised Contrastive Learning with Structure Inference for Graph Classification论文作者:Hao Jia, Junzhong Ji, Minglong Lei论文来源:2022, ArXiv论文地址 阅读全文
posted @ 2022-04-02 21:20 Learner- 阅读(36) 评论(0) 推荐(0) 编辑
摘要: 论文信息 论文标题:Graph-MLP: Node Classification without Message Passing in Graph论文作者:Yang Hu, Haoxuan You, Zhecan Wang, Zhicheng Wang,Erjin Zhou, Yue Gao论文来源 阅读全文
posted @ 2022-04-02 20:16 Learner- 阅读(190) 评论(0) 推荐(0) 编辑
摘要: Paper Information Title:Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text ClassificationAuthors:Jiong Zhang, Wei-Cheng Chang, 阅读全文
posted @ 2022-04-01 22:59 Learner- 阅读(128) 评论(0) 推荐(0) 编辑
摘要: 论文信息 论文标题:Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization论文作者:Wei Dong, Junsheng Wu, Yi Luo, Zongyuan 阅读全文
posted @ 2022-04-01 16:30 Learner- 阅读(312) 评论(0) 推荐(0) 编辑
摘要: 介绍 相对熵(relative entropy),又被称为Kullback-Leibler散度(Kullback-Leibler divergence)或信息散度(information divergence),是两个概率分布(probability distribution)间差异的非对称性度量。 阅读全文
posted @ 2022-04-01 15:09 Learner- 阅读(27) 评论(0) 推荐(0) 编辑
摘要: class torch.optim.Adam(params, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)参数:params (iterable) – 待优化参数的iterable或者是定义了参数组的dictlr (float, 可 阅读全文
posted @ 2022-03-31 09:55 Learner- 阅读(27) 评论(0) 推荐(0) 编辑
摘要: 随机梯度下降法 $\theta_{t} \leftarrow \theta_{t-1}-\alpha g_{t}$ Code: optimzer = torch.optim.SGD(model.parameters(),lr = 0.001) 权重衰减 $\theta_{t} \leftarrow( 阅读全文
posted @ 2022-03-31 09:49 Learner- 阅读(248) 评论(0) 推荐(0) 编辑
摘要: 1 导入实验所需要的包 import torch import torch.nn as nn import torchvision import torchvision.transforms as transforms import torch.nn.functional as F from tor 阅读全文
posted @ 2022-03-30 09:09 Learner- 阅读(41) 评论(0) 推荐(0) 编辑
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