图 书 馆
训练受限玻尔兹曼机器使用逼近似然梯度的近似Training Restricted Boltzmann Machines using Approximations to the Likelihood Gradient |
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| 课程网址: | http://videolectures.net/icml08_tieleman_trb/ |
| 主讲教师: | Tijmen Tieleman |
| 开课单位: | 多伦多大学 |
| 开课时间: | 2008-07-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | A new algorithm for training Restricted Boltzmann Machines is introduced. The algorithm, named Persistent Contrastive Divergence, is different from the standard Contrastive Divergence algorithms in that it aims to draw samples from almost exactly the model distribution. It is compared to some standard Contrastive Divergence algorithms on the tasks of modeling handwritten digits and classifying digit images by learning a model of the joint distribution of images and labels. The Persistent Contrastive Divergence algorithm outperforms other Contrastive Divergence algorithms, and is equally fast and simple. |
| 关 键 词: | 玻尔兹曼机器; 抽取样本; 对比发散算法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-21：lxf |
| 阅读次数: | 70 |