Orthogonally Decoupled Variational Fourier Features
Dalle Molle Institute for Artificial Intelligence (IDSIA)（Dalle Molle人工智能研究所 (IDSIA)）；Università della Svizzera Italiana (USI)（瑞士意大利语区大学 (USI)）；University of Limerick (UL)（利默里克大学 (UL)）
英文摘要：
Sparse inducing points have long been a standard method to fit Gaussian processes to big data. In the last few years, spectral methods that exploit approximations of the covariance kernel have shown to be competitive. In this work we exploit a recently introduced orthogonally decoupled variational basis to combine spectral methods and sparse inducing points methods. We show that the method is competitive with the state-of-the-art on synthetic and on real-world data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
