Stochastic Variational Deep Kernel Learning NIPS 2016

Stochastic Variational Deep Kernel Learning NIPS 2016 Paper: Code: Authors: Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing This work can be used as a plugin to standalone deep networks, with minor additional runtime overhead, in exchange for improved predictive performance, interpretability, and full predictive distributions. SVDKL exploits algebraic structure in deep kernels formed from convoluti