GPflow: A Gaussian process library using TensorFlow

27 October 2016

Papers citing "GPflow: A Gaussian process library using TensorFlow"

50 / 269 papers shown

Title
Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors Liwei Wang Suraj Yerramilli Akshay Iyer D. Apley Ping Zhu Wei Chen 43 25 0 26 Jun 2021
Last Layer Marginal Likelihood for Invariance Learning Pola Schwobel Martin Jørgensen Sebastian W. Ober Mark van der Wilk BDL UQCV 26 28 0 14 Jun 2021
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes Sanyam Kapoor Marc Finzi Ke Alexander Wang A. Wilson 49 11 0 12 Jun 2021
The Fast Kernel Transform J. Ryan Sebastian Ament Carla P. Gomes Anil Damle 20 8 0 08 Jun 2021
Multi-output Gaussian Processes for Uncertainty-aware Recommender Systems Yinchong Yang Florian Buettner BDL 10 6 0 08 Jun 2021
Gaussian Processes on Hypergraphs Thomas Pinder K. Turnbull Christopher Nemeth David Leslie 33 4 0 03 Jun 2021
Empirical Models for Multidimensional Regression of Fission Systems A. Dave Jiankai Yu Jarod N Wilson B. Phillips K. Sun Benoit Forget 8 1 0 30 May 2021
Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection Kieran Wood Stephen J. Roberts S. Zohren 16 21 0 28 May 2021
The Graph Cut Kernel for Ranked Data Michelangelo Conserva M. Deisenroth Sesh Kumar 17 0 0 26 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 18 30 0 10 May 2021
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data M. Lemercier C. Salvi Thomas Cass Edwin V. Bonilla Theodoros Damoulas Terry Lyons 17 24 0 10 May 2021
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information Willie Neiswanger Ke Alexander Wang Stefano Ermon MLAU 42 30 0 19 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
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow John Mcleod Hrvoje Stojić Vincent Adam Dongho Kim Jordi Grau-Moya Peter Vrancx Felix Leibfried OffRL 21 2 0 26 Mar 2021
Detecting Label Noise via Leave-One-Out Cross-Validation Yu-Hang Tang Yuanran Zhu W. A. Jong 25 3 0 21 Mar 2021
Sparse Algorithms for Markovian Gaussian Processes William J. Wilkinson Arno Solin Vincent Adam 19 12 0 19 Mar 2021
The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain F. Simpson A. Boukouvalas Václav Cadek E. Sarkans N. Durrande 17 2 0 11 Mar 2021
Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation Riley J. Hickman Florian Hase L. Roch Alán Aspuru-Guzik 26 4 0 05 Mar 2021
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification Heng Hao H. Moon Sima Didari J. Woo P. Bangert AI4TS 19 0 0 25 Feb 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
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients A. Artemev David R. Burt Mark van der Wilk 23 18 0 16 Feb 2021
Numerical issues in maximum likelihood parameter estimation for Gaussian process interpolation S. Basak S. Petit Julien Bect E. Vázquez 15 14 0 24 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
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior Anh Tong Toan M. Tran Hung Bui Jaesik Choi 14 2 0 21 Dec 2020
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization Valerio Perrone Huibin Shen Aida Zolic I. Shcherbatyi Amr Ahmed ... Barbara Pogorzelska Miroslav Miladinovic K. Kenthapadi Matthias Seeger Cédric Archambeau 45 16 0 15 Dec 2020
Are we Forgetting about Compositional Optimisers in Bayesian Optimisation? Antoine Grosnit Alexander I. Cowen-Rivers Rasul Tutunov Ryan-Rhys Griffiths Jun Wang Haitham Bou-Ammar 19 13 0 15 Dec 2020
Design of Experiments for Verifying Biomolecular Networks Ruby Sedgwick John A. Goertz Molly Stevens Ruth Misener Mark van der Wilk 9 1 0 20 Nov 2020
Understanding Variational Inference in Function-Space David R. Burt Sebastian W. Ober Adrià Garriga-Alonso Mark van der Wilk BDL 19 41 0 18 Nov 2020
Pathwise Conditioning of Gaussian Processes James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 18 58 0 08 Nov 2020
Marginalised Gaussian Processes with Nested Sampling F. Simpson V. Lalchand C. Rasmussen GP 12 10 0 30 Oct 2020
Matérn Gaussian Processes on Graphs Viacheslav Borovitskiy I. Azangulov Alexander Terenin P. Mostowsky M. Deisenroth N. Durrande 13 79 0 29 Oct 2020
Scalable Bayesian Optimization with Sparse Gaussian Process Models Ang Yang 21 0 0 26 Oct 2020
Stationary Activations for Uncertainty Calibration in Deep Learning Lassi Meronen Christabella Irwanto Arno Solin UQCV BDL 14 18 0 19 Oct 2020
Stochastic embeddings of dynamical phenomena through variational autoencoders C. A. García P. Félix J. Presedo A. Otero BDL 24 2 0 13 Oct 2020
Ensembling geophysical models with Bayesian Neural Networks Ushnish Sengupta Matt Amos J. S. Hosking C. Rasmussen M. Juniper P. Young UQCV BDL 6 17 0 07 Oct 2020
Short-term prediction of photovoltaic power generation using Gaussian process regression Y. A. Lawati Jack Kelly D. Stowell 9 2 0 05 Oct 2020
Gaussian Process Molecule Property Prediction with FlowMO Henry B. Moss Ryan-Rhys Griffiths 21 23 0 02 Oct 2020
Stein Variational Gaussian Processes Thomas Pinder Christopher Nemeth David Leslie BDL 22 7 0 25 Sep 2020
An Intuitive Tutorial to Gaussian Process Regression Jie Wang GP 17 74 0 22 Sep 2020
A machine learning approach for efficient multi-dimensional integration B. Yoon 9 8 0 14 Sep 2020
SafePILCO: a software tool for safe and data-efficient policy synthesis Kyriakos Polymenakos Nikitas Rontsis Alessandro Abate Stephen J. Roberts 32 6 0 07 Aug 2020
Convergence of Sparse Variational Inference in Gaussian Processes Regression David R. Burt C. Rasmussen Mark van der Wilk 29 69 0 01 Aug 2020
Multioutput Gaussian Processes with Functional Data: A Study on Coastal Flood Hazard Assessment A. F. López-Lopera D. Idier J. Rohmer François Bachoc 16 22 0 28 Jul 2020
Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation R. Maulik T. Botsas Nesar Ramachandra L. Mason Indranil Pan 6 29 0 23 Jul 2020
Orthogonally Decoupled Variational Fourier Features Dario Azzimonti Manuel Schürch A. Benavoli Marco Zaffalon 13 0 0 13 Jul 2020
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes William J. Wilkinson Paul E. Chang Michael Riis Andersen Arno Solin 16 13 0 12 Jul 2020
Overview of Gaussian process based multi-fidelity techniques with variable relationship between fidelities Loïc Brevault M. Balesdent Ali Hebbal 32 69 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 21 36 0 29 Jun 2020
Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian Processes Ryan-Rhys Griffiths Jake L. Greenfield Aditya R. Thawani Arian R. Jamasb Henry B. Moss Anthony Bourached Penelope Jones William McCorkindale Alexander A. Aldrick Matthew J. Fuchter Alpha A. Lee 37 13 0 28 Jun 2020
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation Victor Picheny Vincent Dutordoir A. Artemev N. Durrande 19 2 0 25 Jun 2020