How Deep Are Deep Gaussian Processes?

30 November 2017

Papers citing "How Deep Are Deep Gaussian Processes?"

28 / 28 papers shown

Title
STRIDE: Sparse Techniques for Regression in Deep Gaussian Processes Simon Urbainczyk Aretha L. Teckentrup Jonas Latz GP 17 0 0 16 May 2025
Spatial Bayesian Neural Networks A. Zammit‐Mangion Michael D. Kaminski Ba-Hien Tran Maurizio Filippone Noel Cressie BDL 20 7 0 16 Nov 2023
Bayes Linear Analysis for Statistical Modelling with Uncertain Inputs Samuel E. Jackson D. Woods 16 0 0 09 May 2023
Bayesian inference with finitely wide neural networks Chi-Ken Lu BDL 37 0 0 06 Mar 2023
Sequential Estimation of Gaussian Process-based Deep State-Space Models Yuhao Liu Marzieh Ajirak P. Djuric 26 12 0 29 Jan 2023
Distributional Gaussian Processes Layers for Out-of-Distribution Detection S. Popescu D. Sharp James H. Cole Konstantinos Kamnitsas Ben Glocker OOD 29 0 0 27 Jun 2022
Relaxed Gaussian process interpolation: a goal-oriented approach to Bayesian optimization S. Petit Julien Bect E. Vázquez 49 1 0 07 Jun 2022
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility Hoileong Lee Fadhel Ayed Paul Jung Juho Lee Hongseok Yang François Caron 46 10 0 17 May 2022
Bayesian Deep Learning with Multilevel Trace-class Neural Networks Neil K. Chada Ajay Jasra K. Law Sumeetpal S. Singh BDL UQCV 83 3 0 24 Mar 2022
Posterior contraction rates for constrained deep Gaussian processes in density estimation and classication François Bachoc A. Lagnoux 26 4 0 14 Dec 2021
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning Chi-Ken Lu Patrick Shafto BDL 27 4 0 01 Oct 2021
Neural Operator: Learning Maps Between Function Spaces Nikola B. Kovachki Zong-Yi Li Burigede Liu Kamyar Azizzadenesheli K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 52 441 0 19 Aug 2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective Geoff Pleiss John P. Cunningham 28 24 0 11 Jun 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 33 124 0 14 May 2021
Deep limits and cut-off phenomena for neural networks B. Avelin A. Karlsson AI4CE 40 2 0 21 Apr 2021
Deep State-Space Gaussian Processes Zheng Zhao M. Emzir Simo Särkkä GP 43 19 0 11 Aug 2020
Blind hierarchical deconvolution Arttu Arjas L. Roininen M. Sillanpää A. Hauptmann 18 4 0 22 Jul 2020
Non-Stationary Multi-layered Gaussian Priors for Bayesian Inversion M. Emzir Sari Lasanen Z. Purisha L. Roininen Simo Särkkä 22 9 0 28 Jun 2020
Likelihood-Free Inference with Deep Gaussian Processes Alexander Aushev Henri Pesonen Markus Heinonen J. Corander Samuel Kaski GP 26 10 0 18 Jun 2020
Deep Gaussian Markov Random Fields Per Sidén Fredrik Lindsten BDL 28 22 0 18 Feb 2020
Walsh-Hadamard Variational Inference for Bayesian Deep Learning Simone Rossi Sébastien Marmin Maurizio Filippone BDL 32 14 0 27 May 2019
Interpretable deep Gaussian processes with moments Chi-Ken Lu Scott Cheng-Hsin Yang Xiaoran Hao Patrick Shafto 23 19 0 27 May 2019
On the well-posedness of Bayesian inverse problems J. Latz 22 48 0 26 Feb 2019
Kernel Flows: from learning kernels from data into the abyss H. Owhadi G. Yoo 18 88 0 13 Aug 2018
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo Marton Havasi José Miguel Hernández-Lobato J. J. Murillo-Fuentes BDL 19 96 0 14 Jun 2018
Posterior Inference for Sparse Hierarchical Non-stationary Models K. Monterrubio-Gómez L. Roininen S. Wade Theo Damoulas Mark Girolami 27 27 0 04 Apr 2018
Dimension-Robust MCMC in Bayesian Inverse Problems Victor Chen Matthew M. Dunlop O. Papaspiliopoulos Andrew M. Stuart 27 36 0 09 Mar 2018
Hyperpriors for Matérn fields with applications in Bayesian inversion L. Roininen Mark Girolami Sari Lasanen M. Markkanen 8 57 0 09 Dec 2016