(28)[AISTATS15] Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing
計劃完成深度學習入門的126篇論文第二十八篇，蒙特利爾大學的Bengio領導關於Joint Learning用於Open-Text研究語義分析及意義表示的論文。 ABSTRACT&INTRODUCTION 摘要 Open-text語義分析器(semantic parsers)的目的是經過推斷相應的語義表示(meaning representation)來解釋天然語言中的任何語句。不幸的是，因爲缺少
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相關文章
- 1. Distributed Representations of Words and Phrases and their Compositionality
- 2. 【論文筆記】Joint Unsupervised Learning of Deep Representations and Image Clusters
- 3. Semantic Flow for Fast and Accurate Scene Parsing
- 4. 自監督學習（四）Joint Unsupervised Learning of Deep Representations and Image Clusters
- 5. 行人再識別：Joint Learning of Single-image and Cross-image Representations for Person Re-identification
- 6. 1607.CVPR-Joint Learning of Single-image and Cross-image Representations for Person ReID 論文筆記
- 7. 《Learning Semantic Concepts and Order for Image and Sentence Matching》
- 8. 論文筆記_2018-ECCV-Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation
- 9. 《Distributed Representations of Words and Phrases and their Compositionality》筆記
- 10. 【論文總結】weakly- and semi-supervised learning of a DCNN for semantic Image Segmentation