41 citations · 138 across the 30 of their papers we have counts for
3 papers · 2 filters
Stationary Activations for Uncertainty Calibration in Deep Learning
Lassi Meronen, Christabella Irwanto, Arno Solin
We introduce a new family of non-linear neural network activation functions that mimic the properties induced by the widely-used Matérn family of kernels in Gaussian process (GP) m…
Fast Variational Learning in State-Space Gaussian Process Models
Paul E. Chang, William J. Wilkinson, Mohammad Emtiyaz Khan +1
Gaussian process (GP) regression with 1D inputs can often be performed in linear time via a stochastic differential equation formulation. However, for non-Gaussian likelihoods, thi…
Deep Residual Mixture Models
Perttu Hämäläinen, Martin Trapp, Tuure Saloheimo +1
We propose Deep Residual Mixture Models (DRMMs), a novel deep generative model architecture. Compared to other deep models, DRMMs allow more flexible conditional sampling: The mode…