Deep Gaussian Processes

2 November 2012

Papers citing "Deep Gaussian Processes"

50 / 191 papers shown

Title
Infinite-Fidelity Coregionalization for Physical Simulation Shibo Li Zihan Wang Robert M. Kirby Shandian Zhe AI4CE 28 6 0 01 Jul 2022
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
Noise Estimation in Gaussian Process Regression S. Ameli S. Shadden 11 5 0 20 Jun 2022
Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling Jiri Hron Roman Novak Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 48 6 0 15 Jun 2022
Relaxed Gaussian process interpolation: a goal-oriented approach to Bayesian optimization S. Petit Julien Bect E. Vázquez 41 1 0 07 Jun 2022
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation Henry B. Moss Sebastian W. Ober Victor Picheny 24 4 0 06 Jun 2022
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification Juan Maroñas Daniel Hernández-Lobato 19 6 0 30 May 2022
Sample-Efficient Optimisation with Probabilistic Transformer Surrogates A. Maraval Matthieu Zimmer Antoine Grosnit Rasul Tutunov Jun Wang H. Ammar 30 2 0 27 May 2022
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation Alec M. Dunton Benjamin W. Priest Amanda Muyskens GP 32 3 0 22 May 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
On the inability of Gaussian process regression to optimally learn compositional functions M. Giordano Kolyan Ray Johannes Schmidt-Hieber 51 12 0 16 May 2022
Bézier Curve Gaussian Processes Ronny Hug S. Becker Wolfgang Hubner Michael Arens Jürgen Beyerer 21 4 0 03 May 2022
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness J. Liu Shreyas Padhy Jie Jessie Ren Zi Lin Yeming Wen Ghassen Jerfel Zachary Nado Jasper Snoek Dustin Tran Balaji Lakshminarayanan UQCV BDL 26 48 0 01 May 2022
Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN R. Yasarla Vishwanath A. Sindagi Vishal M. Patel 40 2 0 23 Apr 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
A Deep Bayesian Neural Network for Cardiac Arrhythmia Classification with Rejection from ECG Recordings Wen-Rang Zhang Xinxin Di Guodong Wei Shijia Geng Zhaoji Fu linda Qiao UQCV BDL 11 2 0 26 Feb 2022
TIML: Task-Informed Meta-Learning for Agriculture Gabriel Tseng Hannah Kerner David Rolnick 19 7 0 04 Feb 2022
Bayesian Optimization of Function Networks Raul Astudillo P. Frazier 32 36 0 31 Dec 2021
Triangulation candidates for Bayesian optimization R. Gramacy Anna Sauer Nathan Wycoff 24 13 0 14 Dec 2021
A Sparse Expansion For Deep Gaussian Processes Liang Ding Rui Tuo Shahin Shahrampour 19 6 0 11 Dec 2021
Uncertainty quantification of a three-dimensional in-stent restenosis model with surrogate modelling Dongwei Ye Pavel S. Zun Valeria Krzhizhanovskaya Alfons G. Hoekstra 27 1 0 11 Nov 2021
Multi-Task Neural Processes Jiayi Shen Xiantong Zhen M. Worring Ling Shao BDL 24 8 0 10 Nov 2021
Spatio-Temporal Variational Gaussian Processes Oliver Hamelijnck William J. Wilkinson Niki A. Loppi Arno Solin Theodoros Damoulas AI4TS 19 31 0 02 Nov 2021
Geometry-Aware Hierarchical Bayesian Learning on Manifolds Yonghui Fan Yalin Wang 18 2 0 30 Oct 2021
Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations Jiayao Zhang Hua Wang Weijie J. Su 35 7 0 11 Oct 2021
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning Chi-Ken Lu Patrick Shafto BDL 27 4 0 01 Oct 2021
Approximate Latent Force Model Inference Jacob Moss Felix L. Opolka Bianca Dumitrascu Pietro Lio 49 3 0 24 Sep 2021
Accurate Remaining Useful Life Prediction with Uncertainty Quantification: a Deep Learning and Nonstationary Gaussian Process Approach Zhaoyi Xu Yanjie Guo J. Saleh 19 25 0 23 Sep 2021
A Latent Restoring Force Approach to Nonlinear System Identification T. Rogers Tobias Friis 29 18 0 22 Sep 2021
MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning Qiang He Yuxun Qu Chen Gong Xinwen Hou OffRL 22 10 0 22 Sep 2021
Non-smooth Bayesian Optimization in Tuning Problems Hengrui Luo J. Demmel Younghyun Cho Xin Li Yang Liu 25 13 0 15 Sep 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 52 440 0 19 Aug 2021
Deep Gaussian Process Emulation using Stochastic Imputation Deyu Ming D. Williamson S. Guillas 11 30 0 04 Jul 2021
Deep Gaussian Processes: A Survey Kalvik Jakkala AI4CE GP BDL 29 19 0 21 Jun 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
Posterior contraction for deep Gaussian process priors G. Finocchio Johannes Schmidt-Hieber 35 11 0 16 May 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 31 124 0 14 May 2021
Exploring Uncertainty in Deep Learning for Construction of Prediction Intervals Yuandu Lai Yucheng Shi Yahong Han Yunfeng Shao Meiyu Qi Bingshuai Li UQCV 38 15 0 27 Apr 2021
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion D. Svendsen Pablo Morales-Álvarez A. Ruescas Rafael Molina Gustau Camps-Valls 30 29 0 16 Apr 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 35 23 0 12 Apr 2021
Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation K. R. M. Fernando Cris P Tsokos 31 53 0 09 Mar 2021
Transferring model structure in Bayesian transfer learning for Gaussian process regression Milan Papez A. Quinn 19 11 0 18 Jan 2021
A Tutorial on Sparse Gaussian Processes and Variational Inference Felix Leibfried Vincent Dutordoir S. T. John N. Durrande GP 42 49 0 27 Dec 2020
Uncertainty-driven ensembles of deep architectures for multiclass classification. Application to COVID-19 diagnosis in chest X-ray images J. E. Arco A. Ortiz J. Ramírez Francisco J. Martínez-Murcia Yudong Zhang Juan M Gorriz UQCV 27 3 0 27 Nov 2020
Pathwise Conditioning of Gaussian Processes James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 18 57 0 08 Nov 2020
Transforming Gaussian Processes With Normalizing Flows Juan Maroñas Oliver Hamelijnck Jeremias Knoblauch Theodoros Damoulas 28 34 0 03 Nov 2020
A Bayesian Perspective on Training Speed and Model Selection Clare Lyle Lisa Schut Binxin Ru Y. Gal Mark van der Wilk 44 24 0 27 Oct 2020
Scalable Gaussian Process Variational Autoencoders Metod Jazbec Matthew Ashman Vincent Fortuin Michael Pearce Stephan Mandt Gunnar Rätsch DRL BDL 21 25 0 26 Oct 2020
UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data Chacha Chen Junjie Liang Fenglong Ma Lucas Glass Jimeng Sun Cao Xiao 21 26 0 22 Oct 2020
Probabilistic Numeric Convolutional Neural Networks Marc Finzi Roberto Bondesan Max Welling BDL AI4TS 29 13 0 21 Oct 2020