77 citations · 584 across the 84 of their papers we have counts for
Showing 2016Show all
2 papers · 1 filter
stat.ML2016★ 1 cited
A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression
Quang Minh Hoang, Trong Nghia Hoang, Kian Hsiang Low
While much research effort has been dedicated to scaling up sparse Gaussian process (GP) models based on inducing variables for big data, little attention is afforded to the other…
cs.LG2016★ 9 cited
DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks
Jie Fu, Hongyin Luo, Jiashi Feng +2
The performance of deep neural networks is well-known to be sensitive to the setting of their hyperparameters. Recent advances in reverse-mode automatic differentiation allow for o…