文獻閱讀——Revisiting Semi-Supervised Learning with Graph Embeddings
文獻閱讀——Revisiting Semi-Supervised Learning with Graph Embeddings 1 Introduction 半監督學習的目標是利用未標記的數據來改善模型的效果。大量半監督的學習算法共同優化利用已標記數據的監督學習損失和利用已標記數據和未標記數據的半監督學習的兩個訓練的目標函數。基於圖的半監督學習定義了一個對於已標記數據和一個圖的拉普拉斯正則化項的損
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相關文章
- 1. 【論文解讀 CIKM 2018 | JUST】Are Meta-Paths Necessary? Revisiting Heterogeneous Graph Embeddings
- 2. 文獻閱讀（16）ICLR2020-On The Equivalence between Node Embeddings and Structural graph representations
- 3. 文獻閱讀 - From Word Embeddings To Document Distances
- 4. 文獻閱讀（14）ACM2018-Learning dynamic embeddings from temporal interactions
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- 6. 圖分類《Hierarchical Graph Representation Learning with Differentiable Pooling》閱讀筆記
- 7. 文獻閱讀報告-Learning Lane Graph Representations for Motion Forecasting
- 8. 文獻閱讀（15）CIKM2015-GraRep：Learning Graph Representations with Global Structural Information
- 9. 閱讀分享：Enriching BERT with Knowledge Graph Embeddings for Document Classification
- 10. Convolutional 2D Knowledge Graph Embeddings 閱讀筆記