Doubly Stochastic Variational Inference for Deep Gaussian Processes

24 May 2017

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

50 / 230 papers shown

Title
Deep Gaussian Processes: A Survey Kalvik Jakkala AI4CE GP BDL 26 19 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 33 11 0 12 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
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition Shengyang Sun Jiaxin Shi A. Wilson Roger C. Grosse BDL 9 6 0 10 Jun 2021
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features Thomas M. McDonald Mauricio A. Alvarez 11 10 0 10 Jun 2021
How to Evaluate Uncertainty Estimates in Machine Learning for Regression? Laurens Sluijterman Eric Cator Tom Heskes UQCV 33 21 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 33 137 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 12 5 0 02 Jun 2021
Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Network Rui Meng Herbert Lee K. Bouchard 11 2 0 01 Jun 2021
Sparse Uncertainty Representation in Deep Learning with Inducing Weights H. Ritter Martin Kukla Chen Zhang Yingzhen Li UQCV BDL 55 17 0 30 May 2021
Hierarchical Non-Stationary Temporal Gaussian Processes With $L^1$ -Regularization Zheng Zhao Rui Gao Simo Särkkä 15 0 0 20 May 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 31 124 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 10 30 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 35 15 0 27 Apr 2021
Convolutional Normalizing Flows for Deep Gaussian Processes Haibin Yu Dapeng Liu Yizhou Chen K. H. Low Patrick Jaillet BDL 25 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 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 33 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 14 12 0 08 Apr 2021
Uncertainty-aware Remaining Useful Life predictor Luca Biggio Alexander Wieland M. A. Chao I. Kastanis Olga Fink AI4CE 17 7 0 08 Apr 2021
Accurate and Reliable Forecasting using Stochastic Differential Equations Peng Cui Zhijie Deng Wenbo Hu Jun Zhu UQCV 38 1 0 28 Mar 2021
The Promises and Pitfalls of Deep Kernel Learning Sebastian W. Ober C. Rasmussen Mark van der Wilk UQCV BDL 21 107 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 10 0 0 07 Feb 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
Active Learning for Deep Gaussian Process Surrogates Annie Sauer R. Gramacy D. Higdon GP AI4CE 17 85 0 15 Dec 2020
Deep Gaussian Processes for geophysical parameter retrieval D. Svendsen Pablo Morales-Álvarez Rafael Molina Gustau Camps-Valls GP 12 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 25 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 51 1,879 0 12 Nov 2020
A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning Hany Abdulsamad Peter Nickl Pascal Klink Jan Peters 8 3 0 10 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 23 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 23 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 10 3 0 01 Nov 2020
Inter-domain Deep Gaussian Processes Tim G. J. Rudner Dino Sejdinovic Yarin Gal 6 11 0 01 Nov 2020
Hierarchical Gaussian Processes with Wasserstein-2 Kernels S. Popescu D. Sharp James H. Cole Ben Glocker 25 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 19 26 0 22 Oct 2020
Probabilistic selection of inducing points in sparse Gaussian processes Anders Kirk Uhrenholt V. Charvet B. S. Jensen 9 12 0 19 Oct 2020
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems Anh Tong Jaesik Choi 29 2 0 19 Oct 2020
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning A. Tompkins Rafael Oliveira F. Ramos 8 6 0 09 Oct 2020
Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models Paul Westermann R. Evins AI4CE 11 42 0 05 Oct 2020
Deep kernel processes Laurence Aitchison Adam X. Yang Sebastian W. Ober BDL 16 41 0 04 Oct 2020
Stein Variational Gaussian Processes Thomas Pinder Christopher Nemeth David Leslie BDL 22 7 0 25 Sep 2020
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables Q. Wang H. V. Hoof BDL 21 42 0 21 Aug 2020
Stochastic Bayesian Neural Networks Abhinav Sagar BDL UQCV 12 0 0 12 Aug 2020
Deep State-Space Gaussian Processes Zheng Zhao M. Emzir Simo Särkkä GP 43 19 0 11 Aug 2020
Multi-speaker Text-to-speech Synthesis Using Deep Gaussian Processes Kentaro Mitsui Tomoki Koriyama Hiroshi Saruwatari 20 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 8 13 0 12 Jul 2020
Overview of Gaussian process based multi-fidelity techniques with variable relationship between fidelities Loïc Brevault M. Balesdent Ali Hebbal 16 69 0 30 Jun 2020
Sparse Gaussian Processes with Spherical Harmonic Features Vincent Dutordoir N. Durrande J. Hensman 24 54 0 30 Jun 2020
Multi-fidelity modeling with different input domain definitions using Deep Gaussian Processes Ali Hebbal Loïc Brevault M. Balesdent El-Ghazali Talbi N. Melab AI4CE 6 36 0 29 Jun 2020
Variational Autoencoding of PDE Inverse Problems Daniel J. Tait Theodoros Damoulas AI4CE 9 12 0 28 Jun 2020