3 citations · 15 across the 6 of their papers we have counts for
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cs.LG2018
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks
Martin Trapp, Robert Peharz, Carl E. Rasmussen +1
While Gaussian processes (GPs) are the method of choice for regression tasks, they also come with practical difficulties, as inference cost scales cubic in time and quadratic in me…
cs.LG2018
Probabilistic Deep Learning using Random Sum-Product Networks
Robert Peharz, Antonio Vergari, Karl Stelzner +4
The need for consistent treatment of uncertainty has recently triggered increased interest in probabilistic deep learning methods. However, most current approaches have severe limi…