Get started
Keyword

inducing points

Description: Representative input locations used to approximate Gaussian processes and other kernel-based models, reducing computational demands in statistical learning.
OpenAlex ID: keywords/inducing-points
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
2018 · Lee, Juho, Lee, Yoonho, et al. · arXiv (Cornell University) · 641
PDF
Scalable Variational Gaussian Process Classification
2014 · Hensman, James, Matthews, Alex, et al. · arXiv (Cornell University) · 370
PDF
Deep Kernel Learning
2015 · Wilson, Andrew Gordon, Hu, Zhiting, et al. · arXiv (Cornell University) · 268
PDF
Multipole Graph Neural Operator for Parametric Partial Differential Equations
2020 · Li, Zongyi, Kovachki, Nikola, et al. · arXiv (Cornell University) · 196
PDF
Stochastic Variational Deep Kernel Learning
2016 · Wilson, Andrew Gordon, Hu, Zhiting, et al. · arXiv (Cornell University) · 152
PDF
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models
2014 · Gal, Yarin, van der Wilk, Mark, et al. · arXiv (Cornell University) · 130
PDF
MCMC for Variationally Sparse Gaussian Processes
2015 · Hensman, James, Matthews, Alexander G. de G., et al. · Graduate School and Research Center in Digital Science (EURECOM) · 74
PDF
Thoughts on Massively Scalable Gaussian Processes
2015 · Wilson, Andrew Gordon, Dann, Christoph, et al. · arXiv (Cornell University) · 71
PDF
Collaborative multi-output Gaussian processes
2014 · Trung Nguyen, Edwin V. Bonilla · 69
Fast Allocation of Gaussian Process Experts
2014 · Trung Nguyen, Edwin V. Bonilla · 52
Variational Inference for Gaussian Process Models with Linear Complexity
2017 · Cheng, Ching-An, Boots, Byron · arXiv (Cornell University) · 50
PDF
Personalized Federated Learning with Gaussian Processes
2021 · Achituve, Idan, Shamsian, Aviv, et al. · arXiv (Cornell University) · 48
PDF
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret
2019 · Calandriello, Daniele, Carratino, Luigi, et al. · arXiv (Cornell University) · 41
PDF
An Efficient Bayesian Optimization Approach for Analog Circuit Synthesis via Sparse Gaussian Process Modeling
2020 · Biao He, Shuhan Zhang, et al. · Design, Automation, and Test in Europe · 39
Understanding and comparing scalable Gaussian process regression for big data
2018 · Haitao Liu, Jianfei Cai, et al. · Knowledge-Based Systems · 39
Constant-Time Predictive Distributions for Gaussian Processes
2018 · Pleiss, Geoff, Gardner, Jacob R., et al. · arXiv (Cornell University) · 39
PDF
Recursive Estimation for Sparse Gaussian Process Regression
2019 · Sch\"urch, Manuel, Azzimonti, Dario, et al. · arXiv (Cornell University) · 35
PDF
Online Sparse Multi-Output Gaussian Process Regression and Learning
2018 · Le Yang, Ke Wang, et al. · IEEE Transactions on Signal and Information Processing over Networks · 34
Empirical Assessment of Deep Gaussian Process Surrogate Models for Engineering Problems
2020 · Dushhyanth Rajaram, Tejas G. Puranik, et al. · Journal of Aircraft · 33
Large-Scale Heteroscedastic Regression via Gaussian Process
2020 · Haitao Liu, Yew-Soon Ong, et al. · IEEE Transactions on Neural Networks and Learning Systems · 30
PDF
Parametric Gaussian Process Regressors
2019 · Jankowiak, Martin, Pleiss, Geoff, et al. · arXiv (Cornell University) · 30
PDF
Variational Dependent Multi-output Gaussian Process Dynamical Systems
2014 · Jing Zhao, Shiliang Sun · Lecture notes in computer science · 29
Bayesian Nonlinear Support Vector Machines for Big Data
2017 · Florian Wenzel, Théo Galy-Fajou, et al. · Lecture notes in computer science · 27
PDF
Deep Multi-Instance Learning with Induced Self-Attention for Medical Image Classification
2020 · Zhenliang Li, Liming Yuan, et al. · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · 26
Variational multinomial logit gaussian process
2012 · Kian Ming A. Chai · Journal of Machine Learning Research · 26
Works count: 243
Citation count: 3,209
Year
Topic
Type