资源分类
》 资源 》论文》Understanding Probabilistic Sparse Gaussian Process Approximations

Understanding Probabilistic Sparse Gaussian Process Approximations

2020-02-05 | |  42 |   38 |   0

Abstract

 Good sparse approximations are essential for practical inference in Gaussian Processes as the computational cost of exact methods is prohibitive for large datasets. The Fully Independent Training Conditional (FITC) and the Variational Free Energy (VFE) approximations are two recent popular methods. Despite superficial similarities, these approximations have surprisingly different theoretical properties and behave differently in practice. We thoroughly investigate the two methods for regression both analytically and through illustrative examples, and draw conclusions to guide practical application.

上一篇：Discriminative Gaifman Models

下一篇：Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...