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学习深层稀疏图形模型的结构Learning the structure of deep sparse graphical models |
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| 课程网址: | http://videolectures.net/aistats2010_wallach_ltsods/ |
| 主讲教师: | Hanna M. Wallach |
| 开课单位: | 马萨诸塞大学 |
| 开课时间: | 2010-06-03 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidden units. The Indian buffet process has been used as a nonparametric Bayesian prior on the structure of a directed belief network with a single infinitely wide hidden layer. Here, we introduce the cascading Indian buffet process (CIBP), which provides a prior on the structure of a layered, directed belief network that is unbounded in both depth and width, yet allows tractable inference. We use the CIBP prior with the nonlinear Gaussian belief network framework to allow each unit to vary its behavior between discrete and continuous representations. We use Markov chain Monte Carlo for inference in this model and explore the structures learned on image data. |
| 关 键 词: | 深层稀疏图形模型; 结构 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-07-13：yumf |
| 阅读次数: | 25 |