图 书 馆
可扩展结构化高斯过程的核插值Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP) |
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| 课程网址: | http://videolectures.net/icml2015_wilson_kernel_interpolation/ |
| 主讲教师: | Andrew Gordon Wilson |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2015-09-27 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs). SKI methods produce kernel approximations for fast computations through kernel interpolation. The SKI framework clarifies how the quality of an inducing point approach depends on the number of inducing (aka interpolation) points, interpolation strategy, and GP covariance kernel. SKI also provides a mechanism to create new scalable kernel methods, through choosing different kernel interpolation strategies. Using SKI, with local cubic kernel interpolation, we introduce KISS-GP, which is 1) more scalable than inducing point alternatives, 2) naturally enables Kronecker and Toeplitz algebra for substantial additional gains in scalability, without requiring any grid data, and 3) can be used for fast and expressive kernel learning. KISS-GP costs O(n) time and storage for GP inference. We evaluate KISS-GP for kernel matrix approximation, kernel learning, and natural sound modelling. |
| 关 键 词: | 高斯过程; 插值策略; 伸缩内核 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-04-16：chenxin01 |
| 最后编审: | 2023-05-21：chenxin01 |
| 阅读次数: | 72 |