《Translating Embeddings for Modeling Multi-relational Data》閱讀筆記
Abstract We propose TransE, embedding entities and relationships of multi-relational data in low-dimensional vector space. It significantly outperforms state-of-the-art methods in link prediction on t
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- 2. 論文淺讀—TransE—《translating-embeddings-for-modeling-multi-relational-data》
- 3. 知識圖譜表示學習 TransE: Translating Embeddings for Modeling Multi-relational Data
- 4. Translating Embeddings for Modeling Multi-relational Data 論文翻譯：多元關係數據嵌入
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相關文章
- 1. PaperReading-GraphEmbedding-TransE-《Translating Embeddings for Modeling Multi-relational Data》
- 2. 論文淺讀—TransE—《translating-embeddings-for-modeling-multi-relational-data》
- 3. 知識圖譜表示學習 TransE: Translating Embeddings for Modeling Multi-relational Data
- 4. Translating Embeddings for Modeling Multi-relational Data 論文翻譯：多元關係數據嵌入
- 5. RGCN-Modeling Relational Data with Graph Convolutional Networks閱讀筆記
- 6. 論文閱讀筆記: Modeling Relational Data with Graph Convolutional Networks
- 7. Modeling-Relational-Data-with-Graph-Convolutional-Networks-閱讀筆記
- 8. Convolutional 2D Knowledge Graph Embeddings 閱讀筆記
- 9. From Word Embeddings To Document Distances 閱讀筆記
- 10. 論文閱讀|NeurIPS 2013：（TransE）Embedding Embedding for Modeling Multi-relational Data