图 书 馆
深高斯过程Deep Gaussian processes |
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| 课程网址: | http://videolectures.net/sahd2014_lawrence_gaussian_processes/ |
| 主讲教师: | Neil D. Lawrence |
| 开课单位: | 雪菲尔大学 |
| 开课时间: | 2014-10-29 |
| 课程语种: | 英语 |
| 中文简介: |  在本文中，我们介绍了深度高斯过程（GP）模型。深度GP是基于高斯过程映射的深度信任网络。数据被建模为多元GP的输出。然后，该高斯过程的输入由另一个GP控制。单层模型等效于标准GP或GP潜在变量模型（GP LVM）。我们通过近似变分边际化在模型中进行推断。这导致了我们用于模型选择（模型的层数和每层节点数）的模型的边际可能性的严格下限。深度置信网络通常使用随机梯度下降法进行优化以应用于相对较大的数据集。即使在数据匮乏的情况下，我们完全的贝叶斯处理也允许应用深度模型。通过变分界线进行模型选择，即使对仅包含150个示例的数字数据集进行建模，也可以证明五层层次结构是合理的。 |
| 课程简介: | In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a deep belief network based on Gaussian process mappings. The data is modeled as the output of a multivariate GP. The inputs to that Gaussian process are then governed by another GP. A single layer model is equivalent to a standard GP or the GP latent variable model (GP-LVM). We perform inference in the model by approximate variational marginalization. This results in a strict lower bound on the marginal likelihood of the model which we use for model selection (number of layers and nodes per layer). Deep belief networks are typically applied to relatively large data sets using stochastic gradient descent for optimization. Our fully Bayesian treatment allows for the application of deep models even when data is scarce. Model selection by our variational bound shows that a five layer hierarchy is justified even when modelling a digit data set containing only 150 examples. |
| 关 键 词: | 高斯模型; 建模 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-10-12：zyk |
| 最后编审: | 2020-10-12：zyk |
| 阅读次数: | 140 |