# DAVIDSTUTZ

Check out our latest research on weakly-supervised 3D shape completion.
08thMAY2015

As Gaussian process prediction is quite slow, scaling in $\mathcal{O}(N^3)$ where $N$ is the number of training samples, literature on speeding up kernel methods is of interest. For example, Williams and Seeger use the Nyström approximation [1] while Rahimi and Recht consider using random Fourier features (MatLab code available) [2]. A quick overview can be found in Byron Boot's slides (second part) used for his class "Statistical Techniques in Robotics" in spring 2015.