“Dongxu Zhang 2015-08-31”版本间的差异
来自cslt Wiki
（→Work done in this week） |
（→Work done in this week） |
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| 第4行： | 第4行： | ||
(1)A more bayesian prior distribution over neural networks, that we can give constraint on a hidden layer so that the hidden | (1)A more bayesian prior distribution over neural networks, that we can give constraint on a hidden layer so that the hidden | ||
layers follows a guassian distribution with a more reasonable mean value, which may be a direction of AAAI. | layers follows a guassian distribution with a more reasonable mean value, which may be a direction of AAAI. | ||
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(2)sequential label learning, which can be a further work with Chaoyuan. | (2)sequential label learning, which can be a further work with Chaoyuan. | ||
| − | |||
(3)A new topic model, the code is done, still need to speed up. | (3)A new topic model, the code is done, still need to speed up. | ||
| − | |||
(4)unbalanced autoencoder, haven't considered in details. | (4)unbalanced autoencoder, haven't considered in details. | ||
* Discuss ideas with Tianyi on RS and finally decide a direction, which is a deep UV structure with content knowledge. | * Discuss ideas with Tianyi on RS and finally decide a direction, which is a deep UV structure with content knowledge. | ||
2015年9月1日 (二) 02:42的版本
Work done in this week
- review papers on bayesian graph, document classification.
- find out some interesting directions.
(1)A more bayesian prior distribution over neural networks, that we can give constraint on a hidden layer so that the hidden layers follows a guassian distribution with a more reasonable mean value, which may be a direction of AAAI. (2)sequential label learning, which can be a further work with Chaoyuan. (3)A new topic model, the code is done, still need to speed up. (4)unbalanced autoencoder, haven't considered in details.
- Discuss ideas with Tianyi on RS and finally decide a direction, which is a deep UV structure with content knowledge.
- help Chaoyuan do the baseline reproduction.
Plan to do next week
- Compare the performance with and without topic distribution constraint. Try adding constraint on different layers.
