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Deep Gaussian Processes (DGPs) are hierarchical generalizations of Gaussian Processes that combine well calibrated uncertainty estimates with the high flexibility of multilayer models.
A Monte Carlo implementation of the EM algorithm and the poor man’s data augmentation algorithms
G. C. Wei and M. A. Tanner · 1990
Earlier work this paper cites.
Probabilistic inference using Markov chain Monte Carlo methods
R. M. Neal · 1993
Earlier work this paper cites.
Gaussian processes for regression
C. K. Williams and C. E. Rasmussen · 1996
Earlier work this paper cites.
Fundamental Statistics for Social Research: Step-by-Step Calculations and Computer Techniques Using SPSS for Windows
D. Cramer · 1998
Earlier work this paper cites.
Expectation propagation for approximate Bayesian inference
T. P. Minka · 2001
Earlier work this paper cites.
A unifying view of sparse approximate Gaussian process regression
J. Quiñonero-Candela and C. E. Rasmussen · 2005
Earlier work this paper cites.
Sparse Gaussian processes using pseudo-inputs
E. Snelson and Z. Ghahramani · 2006
Earlier work this paper cites.
Variational learning of inducing variables in sparse Gaussian processes
M. Titsias · 2009
Earlier work this paper cites.
Handbook of Markov chain Monte Carlo
S. Brooks, A. Gelman, G. Jones, and X.-L. Meng · 2011
Earlier work this paper cites.
Practical variational inference for neural networks
A. Graves · 2011
Earlier work this paper cites.
The Harvard clean energy project: large-scale computational screening and design of organic photovoltaics on the world community grid
J. Hachmann, R. Olivares-Amaya, S. Atahan-Evrenk, C. Amador-Bedolla, R. S. Sánchez-Carrera, A. Gold-Parker, L. Vogt, A. M. Brockway, and A. Aspuru-Guzik · 2011
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Robust multi-class Gaussian process classification
D. Hernández-Lobato, J. M. Hernández-Lobato, and P. Dupont · 2011
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Bayesian learning via stochastic gradient Langevin dynamics
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Deep Gaussian processes
A. Damianou and N. Lawrence · 2013
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On convergence properties of the Monte Carlo EM algorithm
R. C. Neath et al · 2013
Probabilistic backpropagation for scalable learning of Bayesian neural networks
J. M. Hernández-Lobato and R. Adams · 2015
Later among the works it cites.
Deep Gaussian processes for regression using approximate expectation propagation
T. Bui, D. Hernández-Lobato, J. Hernandez-Lobato, Y. Li, and R. Turner · 2016
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Incremental variational sparse Gaussian process regression
C.-A. Cheng and B. Boots · 2016
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Random feature expansions for deep Gaussian processes
K. Cutajar, E. V. Bonilla, P. Michiardi, and M. Filippone · 2016
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Bayesian optimization with robust Bayesian neural networks
J. T. Springenberg, A. Klein, S. Falkner, and F. Hutter · 2016
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Stochastic gradient Hamiltonian Monte Carlo
T. Chen, E. Fox, and C. Guestrin · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2015
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Deep Gaussian processes and variational propagation of uncertainty
A. Damianou · 2015
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MCMC for variationally sparse Gaussian processes
J. Hensman, A. G. Matthews, M. Filippone, and Z. Ghahramani · 2015
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C.-A. Cheng and B. Boots · 2017
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How deep are deep Gaussian processes?
M. M. Dunlop, M. Girolami, A. M. Stuart, and A. L. Teckentrup · 2017
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Learning deep latent Gaussian models with Markov chain Monte Carlo
M. D. Hoffman · 2017
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Doubly stochastic variational inference for deep Gaussian processes
H. Salimbeni and M. Deisenroth · 2017
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