图 书 馆
论深层信念网络的定量分析On the Quantitative Analysis of Deep Belief Networks |
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| 课程网址: | http://videolectures.net/icml08_salakhutdinov_qadb/ |
| 主讲教师: | Ruslan Salakhutdinov |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2008-07-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allowed these models to be applied successfully in many application domains. The main building block of a DBN is a bipartite undirected graphical model called a restricted Boltzmann machine (RBM). Due to the presence of the partition function, model selection, complexity control, and exact maximum likelihood learning in RBM's are intractable. Annealed Importance Sampling (AIS), can be used to efficiently estimate the partition function of an RBM. We present a novel AIS scheme for comparing RBM's with different architectures. We further show how an AIS estimator, along with approximate inference, can be used to estimate a lower bound on the log-probability that a DBN model with multiple hidden layers assigns to the test data. This is, to our knowledge, the first step towards obtaining quantitative results that would allow us to directly assess the performance of Deep Belief Networks as generative models of data. |
| 关 键 词: | 隐藏变量层; 高效贪心算法; 概率 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-22：chenxin |
| 阅读次数: | 58 |