(28)[AISTATS15] Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing
计划完成深度学习入门的126篇论文第二十八篇，蒙特利尔大学的Bengio领导关于Joint Learning用于Open-Text研究语义分析及意义表示的论文。 ABSTRACT&INTRODUCTION 摘要 Open-text语义分析器(semantic parsers)的目的是经过推断相应的语义表示(meaning representation)来解释天然语言中的任何语句。不幸的是，因为缺少
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相关文章
- 1. Distributed Representations of Words and Phrases and their Compositionality
- 2. 【论文笔记】Joint Unsupervised Learning of Deep Representations and Image Clusters
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- 4. 自监督学习（四）Joint Unsupervised Learning of Deep Representations and Image Clusters
- 5. 行人再识别：Joint Learning of Single-image and Cross-image Representations for Person Re-identification
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- 8. 论文笔记_2018-ECCV-Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation
- 9. 《Distributed Representations of Words and Phrases and their Compositionality》笔记
- 10. 【论文总结】weakly- and semi-supervised learning of a DCNN for semantic Image Segmentation