论文笔记：Network Representation Learning with Rich Text Information
network representation learning with rich text information 时间仓促，没有特别仔细的看（之后再更新） 论文的成果有两点： 证明deepwalk其实就等价于matrix factorization（DW可以用MF表示） 其中，W矩阵是在skip-gram中中心词的表达，H是上下文的表达。 M在使用softmax的时候是log（词对（vi，ci
相关文章
- 1. 论文笔记：Representation Learning with Contrastive Predictive Coding
- 2. NRL: network representation learning & NE: network embedding. 相关论文总结
- 3. 《Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation》论文笔记
- 4. 【论文笔记】Representation Learning by Learning to Count
- 5. 【论文笔记】NRL-Heterogeneous Information Network
- 6. 【论文笔记】Learning Deconvolution Network for Semantic Segmentation
- 7. 【论文笔记02】Text Understanding with the Attention Sum Reader Network
- 8. Detecting Text in Natural Image with Connectionist Text Proposal Network论文笔记
- 9. Text Style Transfer论文笔记
- 10. [AAAI2018]Learning Structured Representation Representation for Text Classification via Reinforcemen
- 更多相关文章...
- • SVG
- SVG 教程 - • ASP.NET Razor - 标记 - ASP.NET 教程
- • Tomcat学习笔记（史上最全tomcat学习笔记）
- • Scala 中文乱码解决
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. 论文笔记：Representation Learning with Contrastive Predictive Coding
- 2. NRL: network representation learning & NE: network embedding. 相关论文总结
- 3. 《Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation》论文笔记
- 4. 【论文笔记】Representation Learning by Learning to Count
- 5. 【论文笔记】NRL-Heterogeneous Information Network
- 6. 【论文笔记】Learning Deconvolution Network for Semantic Segmentation
- 7. 【论文笔记02】Text Understanding with the Attention Sum Reader Network
- 8. Detecting Text in Natural Image with Connectionist Text Proposal Network论文笔记
- 9. Text Style Transfer论文笔记
- 10. [AAAI2018]Learning Structured Representation Representation for Text Classification via Reinforcemen