Scaling Multidimensional Gaussian Processes using Projected Additive Approximations

登录免费注册

资源分类

论文
算法
数据集
经验分享
技术动态
行业动态

论文
学习
研究领域

算法
学习
研究领域

数据集
自动驾驶
图片

经验分享
学习
研究领域

技术动态
计算机视觉
自然语言处理

行业动态
教育
语音识别

》资源》论文》Scaling Multidimensional Gaussian Processes using Projected Additive Approximations

Scaling Multidimensional Gaussian Processes using Projected Additive Approximations

2020-03-02 |

53 |

34 |

Scaling Multidimensional Gaussian Processes using Projected Additive Approximations
论文

Abstract

Exact Gaussian Process (GP) regression has O(N 3 ) runtime for data size N , making it intractable for large N . Advances in GP scaling have not been extended to the multidimensional input setting, despite the preponderance of multidimensional applications. This paper introduces and tests a novel method of projected additive approximation to multidimensional GPs. We illustrate the power of this method on several datasets, achieving performance close to the naive Full GP at orders of magnitude less cost.

上一篇：Fast Semidifferential-based Submodular Function Optimization

下一篇：LDA Topic Model with Soft Assignment of Descriptors to Words

用户评价