Understanding Probabilistic Sparse Gaussian Process Approximations

NeurIPS 2016 Matthias BauerMark van der WilkCarl Edward Rasmussen

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... (read more)

PDF Abstract NeurIPS 2016 PDF NeurIPS 2016 Abstract

Code


No code implementations yet. Submit your code now

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet