文献阅读——Revisiting Semi-Supervised Learning with Graph Embeddings

时间 2021-01-02

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文献阅读——Revisiting Semi-Supervised Learning with Graph Embeddings 1 Introduction 半监督学习的目标是利用未标记的数据来改善模型的效果。大量半监督的学习算法共同优化利用已标记数据的监督学习损失和利用已标记数据和未标记数据的半监督学习的两个训练的目标函数。基于图的半监督学习定义了一个对于已标记数据和一个图的拉普拉斯正则化项的损

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1. 【论文解读 CIKM 2018 | JUST】Are Meta-Paths Necessary? Revisiting Heterogeneous Graph Embeddings
2. 文献阅读（16）ICLR2020-On The Equivalence between Node Embeddings and Structural graph representations
3. 文献阅读 - From Word Embeddings To Document Distances
4. 文献阅读（14）ACM2018-Learning dynamic embeddings from temporal interactions
5. 论文阅读笔记《Few-Shot Learning with Metric-Agnostic Conditional Embeddings》
6. 图分类《Hierarchical Graph Representation Learning with Differentiable Pooling》阅读笔记
7. 文献阅读报告-Learning Lane Graph Representations for Motion Forecasting
8. 文献阅读（15）CIKM2015-GraRep：Learning Graph Representations with Global Structural Information
9. 阅读分享：Enriching BERT with Knowledge Graph Embeddings for Document Classification
10. Convolutional 2D Knowledge Graph Embeddings 阅读笔记

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