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Doubly Stochastic Variational Inference for Deep Gaussian Processes

24 May 2017

Papers citing "Doubly Stochastic Variational Inference for Deep Gaussian Processes"

50 / 232 papers shown

Title
Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression Ayush Jain P. K. Srijith Mohammad Emtiyaz Khan BDL GP 45 0 0 17 Jul 2021
Input Dependent Sparse Gaussian Processes B. Jafrasteh Carlos Villacampa-Calvo Daniel Hernández-Lobato UQCV 53 5 0 15 Jul 2021
Deep Gaussian Process Emulation using Stochastic Imputation Deyu Ming D. Williamson S. Guillas 67 30 0 04 Jul 2021
Deep Gaussian Processes: A Survey Kalvik Jakkala AI4CE GP BDL 78 20 0 21 Jun 2021
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes Sanyam Kapoor Marc Finzi Ke Alexander Wang A. Wilson 74 12 0 12 Jun 2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective Geoff Pleiss John P. Cunningham 73 27 0 11 Jun 2021
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition Shengyang Sun Jiaxin Shi A. Wilson Roger C. Grosse BDL 31 7 0 10 Jun 2021
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features Thomas M. McDonald Mauricio A. Alvarez 85 10 0 10 Jun 2021
How to Evaluate Uncertainty Estimates in Machine Learning for Regression? Laurens Sluijterman Eric Cator Tom Heskes UQCV 75 24 0 07 Jun 2021
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning Jannik Kossen Neil Band Clare Lyle Aidan Gomez Tom Rainforth Y. Gal OOD 3DPC 120 142 0 04 Jun 2021
Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes Ji Won Park A. Villar Yin Li Yan-Fei Jiang S. Ho J. Lin P. Marshall A. Roodman BDL 43 5 0 02 Jun 2021
Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Network Rui Meng Herbert Lee K. Bouchard 46 2 0 01 Jun 2021
Sparse Uncertainty Representation in Deep Learning with Inducing Weights H. Ritter Martin Kukla Chen Zhang Yingzhen Li UQCV BDL 90 17 0 30 May 2021
Hierarchical Non-Stationary Temporal Gaussian Processes With $L^1$ -Regularization Zheng Zhao Rui Gao Simo Särkkä 46 0 0 20 May 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 137 134 0 14 May 2021
Deep Neural Networks as Point Estimates for Deep Gaussian Processes Vincent Dutordoir J. Hensman Mark van der Wilk Carl Henrik Ek Zoubin Ghahramani N. Durrande BDL UQCV 106 31 0 10 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 65 15 0 27 Apr 2021
Convolutional Normalizing Flows for Deep Gaussian Processes Haibin Yu Dapeng Liu Yizhou Chen K. H. Low Patrick Jaillet BDL 64 6 0 17 Apr 2021
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion D. Svendsen Pablo Morales-Álvarez A. Ruescas Rafael Molina Gustau Camps-Valls 141 30 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 89 23 0 12 Apr 2021
Residual Gaussian Process: A Tractable Nonparametric Bayesian Emulator for Multi-fidelity Simulations Wei W. Xing A. Shah Peng Wang Shandian Zhe Robert M. Kirby 61 13 0 08 Apr 2021
Uncertainty-aware Remaining Useful Life predictor Luca Biggio Alexander Wieland M. A. Chao I. Kastanis Olga Fink AI4CE 32 7 0 08 Apr 2021
Accurate and Reliable Forecasting using Stochastic Differential Equations Peng Cui Zhijie Deng Wenbo Hu Jun Zhu UQCV 75 1 0 28 Mar 2021
The Promises and Pitfalls of Deep Kernel Learning Sebastian W. Ober C. Rasmussen Mark van der Wilk UQCV BDL 82 109 0 24 Feb 2021
Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty Simon Olofsson Eduardo S. Schultz A. Mhamdi Alexander Mitsos M. Deisenroth Ruth Misener 17 0 0 07 Feb 2021
A Tutorial on Sparse Gaussian Processes and Variational Inference Felix Leibfried Vincent Dutordoir S. T. John N. Durrande GP 162 52 0 27 Dec 2020
Active Learning for Deep Gaussian Process Surrogates Annie Sauer R. Gramacy D. Higdon GP AI4CE 88 93 0 15 Dec 2020
Deep Gaussian Processes for geophysical parameter retrieval D. Svendsen Pablo Morales-Álvarez Rafael Molina Gustau Camps-Valls GP 45 4 0 07 Dec 2020
Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning Chao Du Zhifeng Gao Shuo Yuan Lining Gao Z. Li Yifan Zeng Xiaoqiang Zhu Jian Xu Kun Gai Kuang-chih Lee 96 18 0 25 Nov 2020
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges Moloud Abdar Farhad Pourpanah Sadiq Hussain Dana Rezazadegan Li Liu ... Xiaochun Cao Abbas Khosravi U. Acharya V. Makarenkov S. Nahavandi BDL UQCV 369 1,947 0 12 Nov 2020
A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning Hany Abdulsamad Peter Nickl Pascal Klink Jan Peters 23 3 0 10 Nov 2020
Pathwise Conditioning of Gaussian Processes James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 109 61 0 08 Nov 2020
Transforming Gaussian Processes With Normalizing Flows Juan Maroñas Oliver Hamelijnck Jeremias Knoblauch Theodoros Damoulas 105 34 0 03 Nov 2020
Sample-efficient reinforcement learning using deep Gaussian processes Charles W. L. Gadd Markus Heinonen Harri Lähdesmäki Samuel Kaski GP BDL 64 4 0 02 Nov 2020
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes Tim G. J. Rudner Oscar Key Y. Gal Tom Rainforth 18 3 0 01 Nov 2020
Inter-domain Deep Gaussian Processes Tim G. J. Rudner Dino Sejdinovic Yarin Gal 79 11 0 01 Nov 2020
Hierarchical Gaussian Processes with Wasserstein-2 Kernels S. Popescu D. Sharp James H. Cole Ben Glocker 74 5 0 28 Oct 2020
UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data Chacha Chen Junjie Liang Fenglong Ma Lucas Glass Jimeng Sun Cao Xiao 71 26 0 22 Oct 2020
Probabilistic selection of inducing points in sparse Gaussian processes Anders Kirk Uhrenholt V. Charvet B. S. Jensen 48 13 0 19 Oct 2020
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems Anh Tong Jaesik Choi 60 2 0 19 Oct 2020
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning A. Tompkins Rafael Oliveira F. Ramos 63 6 0 09 Oct 2020
Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models Paul Westermann R. Evins AI4CE 44 44 0 05 Oct 2020
Deep kernel processes Laurence Aitchison Adam X. Yang Sebastian W. Ober BDL 96 42 0 04 Oct 2020
Stein Variational Gaussian Processes Thomas Pinder Christopher Nemeth David Leslie BDL 46 7 0 25 Sep 2020
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables Q. Wang H. V. Hoof BDL 66 42 0 21 Aug 2020
Stochastic Bayesian Neural Networks Abhinav Sagar BDL UQCV 48 0 0 12 Aug 2020
Deep State-Space Gaussian Processes Zheng Zhao M. Emzir Simo Särkkä GP 88 19 0 11 Aug 2020
Multi-speaker Text-to-speech Synthesis Using Deep Gaussian Processes Kentaro Mitsui Tomoki Koriyama Hiroshi Saruwatari 48 5 0 07 Aug 2020
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes William J. Wilkinson Paul E. Chang Michael Riis Andersen Arno Solin 58 13 0 12 Jul 2020
Overview of Gaussian process based multi-fidelity techniques with variable relationship between fidelities Loïc Brevault M. Balesdent Ali Hebbal 90 72 0 30 Jun 2020