3 citations · 15 across the 6 of their papers we have counts for
3 papers · 1 filter
Deep Structured Mixtures of Gaussian Processes
Martin Trapp, Robert Peharz, Franz Pernkopf +1
Gaussian Processes (GPs) are powerful non-parametric Bayesian regression models that allow exact posterior inference, but exhibit high computational and memory costs. In order to i…
Optimisation of Overparametrized Sum-Product Networks
Martin Trapp, Robert Peharz, Franz Pernkopf
It seems to be a pearl of conventional wisdom that parameter learning in deep sum-product networks is surprisingly fast compared to shallow mixture models. This paper examines the…
Bayesian Learning of Sum-Product Networks
Martin Trapp, Robert Peharz, Hong Ge +2
Sum-product networks (SPNs) are flexible density estimators and have received significant attention due to their attractive inference properties. While parameter learning in SPNs i…