“Zhiyong Zhang”版本间的差异
来自cslt Wiki
（以“==Task schedual== ===LM development=== ==To Do== === To Read papers===”为内容创建页面） |
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| （2位用户的16个中间修订版本未显示） | |||
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| − | === | + | =Papers To Read = |
| + | * 1, Learned-Norm pooling for deep feedforward and recurrent neural networks | ||
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| + | |||
| + | =Task schedules= | ||
| + | |||
| + | ==Summary== | ||
| + | -------------------------------------------------------------------------------------------------------- | ||
| + | Priority | Tasks name | Status | Notions | ||
| + | -------------------------------------------------------------------------------------------------------- | ||
| + | 1 | Bi-Softmax | ■■■□□□□□□□ | 1400h am training and problem fixing | ||
| + | -------------------------------------------------------------------------------------------------------- | ||
| + | 2 | RNN+DAE | □□□□□□□□□□ | | ||
| + | -------------------------------------------------------------------------------------------------------- | ||
| + | |||
| + | ==Speech Recognition== | ||
| + | ===Multi-lingual Am training=== | ||
| + | ====Bi-Softmax==== | ||
| + | * Using two distinct softmax for English and Chinese data. | ||
| + | * Testing on 100h-Ch+100h-En, better performance observed. | ||
| + | * Now testing the source code on 1400h_8k data, but stange decoding results got.Need to further investigate. | ||
| + | |||
| + | =Reading Lists= | ||
| + | *[[媒体文件:Efficient_mini-batch_training_for_stochastic_optimization.pdf |苏圣 2015-10-29 Efficient_mini-batch_training_for_stochastic_optimization ]] | ||
| + | *[[媒体文件:2015_Fitnets-Hints for thin deep nets.pdf |张之勇 2015-10-29 2015_Fitnets-Hints for thin deep nets ]] | ||
| + | *http://www.cs.cmu.edu/~muli/file/minibatch_sgd.pdf | ||
2015年10月29日 (四) 07:14的最后版本
目录
Papers To Read
- 1, Learned-Norm pooling for deep feedforward and recurrent neural networks
Task schedules
Summary
--------------------------------------------------------------------------------------------------------
Priority | Tasks name | Status | Notions
--------------------------------------------------------------------------------------------------------
1 | Bi-Softmax | ■■■□□□□□□□ | 1400h am training and problem fixing
--------------------------------------------------------------------------------------------------------
2 | RNN+DAE | □□□□□□□□□□ |
--------------------------------------------------------------------------------------------------------
Speech Recognition
Multi-lingual Am training
Bi-Softmax
- Using two distinct softmax for English and Chinese data.
- Testing on 100h-Ch+100h-En, better performance observed.
- Now testing the source code on 1400h_8k data, but stange decoding results got.Need to further investigate.
