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Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product
  Networks

Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks

12 September 2018
Martin Trapp
Robert Peharz
C. Rasmussen
Franz Pernkopf
    TPMGP
ArXiv (abs)PDFHTML

Papers citing "Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks"

5 / 5 papers shown
Title
A Unified Approach for Learning the Parameters of Sum-Product Networks
A Unified Approach for Learning the Parameters of Sum-Product Networks
Han Zhao
Pascal Poupart
Geoffrey J. Gordon
TPM
57
70
0
03 Jan 2016
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
Markus Heinonen
Henrik Mannerstrom
Juho Rousu
Samuel Kaski
Harri Lähdesmäki
54
103
0
18 Aug 2015
Improving the Gaussian Process Sparse Spectrum Approximation by
  Representing Uncertainty in Frequency Inputs
Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs
Y. Gal
Richard Turner
64
78
0
09 Mar 2015
Distributed Gaussian Processes
Distributed Gaussian Processes
M. Deisenroth
Jun Wei Ng
GP
75
342
0
10 Feb 2015
Gaussian Processes for Big Data
Gaussian Processes for Big Data
J. Hensman
Nicolò Fusi
Neil D. Lawrence
GP
110
1,237
0
26 Sep 2013
1