Deep Gaussian processes and variational propagation of uncertainty

Keywords

Latent variable, Gaussian process, Inference, Deep learning, Artificial intelligence

References

• Statistical Factor Analysis and Related MethodsAlexander Basilevsky — 1994
• Unsupervised Feature Learning and Deep Learning: A Review and New PerspectivesYoshua Bengio, Aaron Courville, Pascal Vincent — 2012
• Bayesian Gaussian Process Latent Variable ModelMichalis K. Titsias, Neil D. Lawrence — 2010
• Bayesian Filtering and SmoothingSimo Srkk — 2013
• Nested Variational Compression in Deep Gaussian ProcessesJames Hensman, Neil D. Lawrence — 2014
• Variational Learning of Inducing Variables in Sparse Gaussian ProcessesMichalis K. Titsias — 2009
• Semisupervised alignment of manifoldsJihun Ham, Daniel D. Lee, Lawrence K. Saul — 2005
• Learning for Larger Datasets with the Gaussian Process Latent Variable ModelNeil D. Lawrence — 2007
• Pattern Recognition and Machine LearningChris Bishop — 2006
• Fast Forward Selection to Speed Up Sparse Gaussian Process RegressionMatthias Seeger, Christopher K. I. Williams, Neil D. Lawrence — 2003
• A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New ModelsNeil D. Lawrence — 2010
• Factorized Orthogonal Latent SpacesMathieu Salzmann, Carl Henrik Ek, Raquel Urtasun — 2010
• A Unifying View of Sparse Approximate Gaussian Process RegressionJoaquin Quiñonero-Candela, Carl Edward Rasmussen — 2005
• Variational Inducing Kernels for Sparse Convolved Multiple Output G aussian ProcessesMauricio A. Álvarez, David Luengo, Michalis K. Titsias — 2009
• Efficient Multioutput Gaussian Processes through Variational Inducing KernelsMauricio A. Álvarez, David Luengo, Michalis K. Titsias — 2010
• The Geometry Of Kernel Canonical Correlation AnalysisMalte Kuß — 2003
• A Survey on Multi-view LearningChang Xu, Dacheng Tao, Chao Xu — 2013
• Ockham's Razor and Bayesian AnalysisW. H. Jefferys, James O. Berger — 1992
• Manifold Gaussian Processes for regressionRoberto Calandra, Jan Peters, Carl Edward Rasmussen — 2014
• Gaussian Process Models with Parallelization and GPU accelerationZhenwen Dai, Andreas Damianou, James Hensman — 2014

Cited by

• Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic BackpropagationThang D. Bui, José Miguel Hernández-Lobato, Yingzhen Li — 2015
• Deep Gaussian Processes for Regression using Approximate Expectation PropagationThang D. Bui, José Miguel Hernández-Lobato, Daniel Hernández-Lobato — 2016
• Inverse Reinforcement Learning via Deep Gaussian ProcessMing Jin, Andreas Damianou, Pieter Abbeel — 2015
• Variational Inference for Latent Variables and Uncertain Inputs in Gaussian ProcessesAndreas Damianou, Michalis K. Titsias, Neil D. Lawrence — 2016
• Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor AnalysisAndreas Damianou, Neil D. Lawrence, Carl Henrik Ek — 2016
• Deep Multi-fidelity Gaussian ProcessesMaziar Raissi, George Em Karniadakis — 2016
• Latent Autoregressive Gaussian Processes Models for Robust System IdentificationCésar Mattos, Andreas Damianou, Guilherme A. Barreto — 2016
• An integrated probabilistic framework for robot perception, learning and memoryUriel Martínez-Hernández, Andreas Damianou, Daniel Camilleri — 2016
• Nonlinear information fusion algorithms for data-efficient multi-fidelity modellingParis Perdikaris, Maziar Raissi, Andreas Damianou — 2017
• Deep recurrent Gaussian processes for outlier-robust system identificationCésar Mattos, Zhenwen Dai, Andreas Damianou — 2017
• Gaussian process based approaches for survival analysisAlan D. Saul — 2016
• Remote Sensing Image Classification With Large-Scale Gaussian ProcessesPablo Morales-Álvarez, Adrián Pérez-Suay, Rafael Molina — 2017
• Deep recurrent Gaussian process with variational Sparse Spectrum approximationRoman Föll, Bernard Haasdonk, Markus Hanselmann — 2017
• Bringing models to the domain: deploying Gaussian processes in the biological sciencesMax Zwiessele — 2017
• Efficient remote sensing image classification with Gaussian processes and Fourier featuresPablo Morales-Álvarez, Adrián Pérez-Suay, Rafael Molina — 2017
• Multi-Fidelity Reinforcement Learning with Gaussian ProcessesVarun Suryan, Nahush Gondhalekar, Pratap Tokekar — 2017
• Probabilistic modelling and reconstruction of strainCarl Jidling, Johannes Hendriks, Niklas Wahlström — 2018
• The Gaussian Process Autoregressive Regression Model (GPAR)James Requeima, Will Tebbutt, Wessel P. Bruinsma — 2018
• Pseudo-marginal Bayesian inference for supervised Gaussian process latent variable modelsCharles W. L. Gadd, Sara Wade, A.A. Shah — 2018
• Bayesian Active Learning for Choice Models With Deep Gaussian ProcessesJie Yang, Diego Klabjan — 2018

Related papers

• Variational Learning of Inducing Variables in Sparse Gaussian ProcessesMichalis K. Titsias — 2009
• Gaussian Processes for Machine LearningCarl Edward Rasmussen, Christopher K. I. Williams — 2005
• Bayesian Gaussian Process Latent Variable ModelMichalis K. Titsias, Neil D. Lawrence — 2010
• Sparse Gaussian Processes using Pseudo-inputsEdward Snelson, Zoubin Ghahramani — 2005
• Avoiding pathologies in very deep networksDavid Duvenaud, Oren Rippel, Ryan P. Adams — 2014
• A Unifying View of Sparse Approximate Gaussian Process RegressionJoaquin Quiñonero-Candela, Carl Edward Rasmussen — 2005
• Nested Variational Compression in Deep Gaussian ProcessesJames Hensman, Neil D. Lawrence — 2014