Inference in Deep Gaussian Processes using Stochastic Gradient
Hamiltonian Monte Carlo

Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo

14 June 2018

José Miguel Hernández-Lobato

J. J. Murillo-Fuentes

Papers citing "Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo"

14 / 14 papers shown

Title
Adaptive RKHS Fourier Features for Compositional Gaussian Process Models Xinxing Shi Thomas Baldwin-McDonald Mauricio A. Álvarez 71 0 0 01 Jul 2024
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial V. Nemani Luca Biggio Xun Huan Zhen Hu Olga Fink Anh Tran Yan Wang Xiaoge Zhang Chao Hu AI4CE 30 75 0 07 May 2023
Free-Form Variational Inference for Gaussian Process State-Space Models Xuhui Fan Edwin V. Bonilla T. O’Kane S. Sisson 11 8 0 20 Feb 2023
The Neural Process Family: Survey, Applications and Perspectives Saurav Jha Dong Gong Xuesong Wang Richard E. Turner L. Yao BDL 73 24 0 01 Sep 2022
Distributional Gaussian Processes Layers for Out-of-Distribution Detection S. Popescu D. Sharp James H. Cole Konstantinos Kamnitsas Ben Glocker OOD 21 0 0 27 Jun 2022
A Sparse Expansion For Deep Gaussian Processes Liang Ding Rui Tuo Shahin Shahrampour 11 6 0 11 Dec 2021
Probabilistic Metamodels for an Efficient Characterization of Complex Driving Scenarios Max Winkelmann Mike Kohlhoff H. Tadjine Steffen Müller 21 9 0 06 Oct 2021
Deep Gaussian Processes: A Survey Kalvik Jakkala AI4CE GP BDL 10 19 0 21 Jun 2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective Geoff Pleiss John P. Cunningham 26 24 0 11 Jun 2021
GPflux: A Library for Deep Gaussian Processes Vincent Dutordoir Hugh Salimbeni Eric Hambro John Mcleod Felix Leibfried A. Artemev Mark van der Wilk J. Hensman M. Deisenroth S. T. John GP 28 23 0 12 Apr 2021
Likelihood-Free Inference with Deep Gaussian Processes Alexander Aushev Henri Pesonen Markus Heinonen J. Corander Samuel Kaski GP 20 10 0 18 Jun 2020
Implicit Posterior Variational Inference for Deep Gaussian Processes Haibin Yu Yizhou Chen Zhongxiang Dai K. H. Low Patrick Jaillet 11 42 0 26 Oct 2019
Learning GPLVM with arbitrary kernels using the unscented transformation Daniel Augusto R. M. A. de Souza Diego Mesquita C. L. C. Mattos Joao P. P. Gomes 13 0 0 03 Jul 2019
MCMC using Hamiltonian dynamics Radford M. Neal 185 3,262 0 09 Jun 2012