Multihop Atention Networks for QA Matching
文章链接：MAN 原作代码地址：pytorch 出处：SIGIR’18, July 8-12, 2018, Ann Arbor, MI, USA 推荐理由： 作者在introduction和related work中把前人的工作介绍得非常详细，诚意满满，至少在我眼里看来并不是在划水，比较适合入门。 作者想法比较新颖，利用动态记忆网络（DMNS）的思想进行建模，同时作者摒弃了在AS任务中用co-at
相关文章
- 1. Matching Networks for One Shot Learning
- 2. 论文《Matching Networks for One Shot Learning》阅读
- 3. [CVPR 2019] Gradient Matching Generative Networks for Zero-Shot Learning
- 4. Matching Networks for One Shot Learning论文解读
- 5. 【One Shot】《Matching Networks for One Shot Learning》
- 6. 《Dual Attention Networks for Multimodal Reasoning and Matching》
- 7. Matching Networks for One Shot Learning论文分析
- 8. Matching Networks for one Shot Learning 阅读笔记
- 9. QA for PI - One stop for all
- 10. Predicting Multi-step Citywide Passenger Demands Using Atention-based Neural Networks
- 更多相关文章...
- • Swift for 循环 - Swift 教程
- • Scala for循环 - Scala教程
- • PHP开发工具
- • C# 中 foreach 遍历的用法
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. Matching Networks for One Shot Learning
- 2. 论文《Matching Networks for One Shot Learning》阅读
- 3. [CVPR 2019] Gradient Matching Generative Networks for Zero-Shot Learning
- 4. Matching Networks for One Shot Learning论文解读
- 5. 【One Shot】《Matching Networks for One Shot Learning》
- 6. 《Dual Attention Networks for Multimodal Reasoning and Matching》
- 7. Matching Networks for One Shot Learning论文分析
- 8. Matching Networks for one Shot Learning 阅读笔记
- 9. QA for PI - One stop for all
- 10. Predicting Multi-step Citywide Passenger Demands Using Atention-based Neural Networks