“Chao Xing 2015-12-7”版本间的差异
来自cslt Wiki
| 第6行： | 第6行： | ||
Plan to do: | Plan to do: | ||
| − | + | 1. Reproduce RNN picture model with specified image. | |
| − | 2 | + | 2. Done with Matrix Factorization. |
| − | + | 3. Start to investigate word segmentation. | |
This Week: | This Week: | ||
| 第16行： | 第16行： | ||
What have done: | What have done: | ||
| − | 1. Investigate DRAW with RNN-lstm and RNN-at-lstm by input same random sample. | + | 1. Investigate DRAW with RNN-lstm and RNN-at-lstm by input same random sample. |
| − | 2. Investigate word sense vectors. Try to find some strategy among balance word frequency, word sense vectors, and neural computing. | + | 2. Investigate word sense vectors. Try to find some strategy among balance word frequency, word sense vectors, and neural computing. |
Plan to do: | Plan to do: | ||
| − | 1. Broadcast last week mnist results to Chinese images. | + | 1. Broadcast last week mnist results to Chinese images. |
| − | 2. Give a report after read some papers. | + | 2. Give a report after read some papers. |
| − | 3. Test some word sense vector models. | + | 3. Test some word sense vector models. |
2015年12月7日 (一) 01:23的最后版本
Last Week:
Solution:
- None.
Plan to do:
1. Reproduce RNN picture model with specified image.
2. Done with Matrix Factorization.
3. Start to investigate word segmentation.
This Week:
What have done:
1. Investigate DRAW with RNN-lstm and RNN-at-lstm by input same random sample.
2. Investigate word sense vectors. Try to find some strategy among balance word frequency, word sense vectors, and neural computing.
Plan to do:
1. Broadcast last week mnist results to Chinese images.
2. Give a report after read some papers.
3. Test some word sense vector models.
