“Tianyi Luo 2016-04-25”版本间的差异
来自cslt Wiki
第22行: | 第22行: | ||
=== Plan to do next week === | === Plan to do next week === | ||
* To implement tensorflow version of RNN/LSTM Max margin vector training. | * To implement tensorflow version of RNN/LSTM Max margin vector training. | ||
+ | * To implement attention chatting model with xiaobing corpus. | ||
===Interested papers === | ===Interested papers === | ||
*Cascading Bandits: Learning to Rank in the Cascade Model(ICML 2015) [[http://zheng-wen.com/Cascading_Bandit_Paper.pdf pdf]] | *Cascading Bandits: Learning to Rank in the Cascade Model(ICML 2015) [[http://zheng-wen.com/Cascading_Bandit_Paper.pdf pdf]] |
2016年4月24日 (日) 13:21的版本
Plan to do this week
- To implement tensorflow version of RNN/LSTM Max margin vector training.
Work done in this week
2016-04-18
- Optimize theano version of Generationg the similar questions' vectors based on RNN.
- Finish implementing theano version of LSTM Max margin vector training.
2016-04-19
- Optimize theano version of Generationg the similar questions' vectors based on RNN.
2016-04-20
- Finish submiting the camera version paper of IJCAI 2016.
- Update the version of Technical Report about Chinese Song Iambics generation.
2016-04-21
- Finish helping Teacher Wang to prepare for text group's presentation(Tang poetry and Songci generation and Intelligent QA system) for Tsinghua University's 105 anniversary.
- Submit our IJCAI paper to arxiv. (Solve a big problem about submitting the paper including Chinese chacracters)
- Optimize theano version of Generationg the similar questions' vectors based on RNN.
2016-04-22
- Speed up process of the test performance about theano version of Generationg the similar questions' vectors based on RNN.
2016-04-23
- Use entity match rules to improve the accuracy from 38% to 58%.
2016-04-24
- Use entity match rules to improve the accuracy from 58% to 72%.
Plan to do next week
- To implement tensorflow version of RNN/LSTM Max margin vector training.
- To implement attention chatting model with xiaobing corpus.
Interested papers
- Cascading Bandits: Learning to Rank in the Cascade Model(ICML 2015) [pdf]